Activity-Based Online Learning: A Response to Dyslexia and COVID

*Charles Potter*

### **Abstract**

Dr. Charles Potter's Reading Fluency Programme implements individual learning programmes focusing on children's learning needs. The methods and materials can be used in the treatment of dyslexia, as well as for working with children with reading, writing, and spelling difficulties or difficulties with rate of work at school. The programmes are activity-based, and are introduced through online sessions related to the child's individual learning needs as identified through initial assessment and ongoing evaluation. Based on assessment, an individual programme is developed for the child, focusing on areas of need. The programme then uses electronic books, activity books and materials for treatment of phonological and phonemic difficulties, phonic difficulties, as well as linked problems with reading, writing, spelling, reading comprehension and working memory development. This chapter provides theoretical background on the neurolinguistic basis of the programme's methods and materials, which have been developed internationally and implemented pre COVID with both first and second language speakers of English. It also provides information on how the materials have been implemented post COVID using activity-based online learning formats, and the results of children based on pre and post assessments.

**Keywords:** dyslexia, reading, writing, spelling, working memory, electronic materials, activity-based online learning

### **1. Introduction**

Dr. Charles Potter's Reading Programme has been previously described in a number of publications. These have documented the theory behind the development of the programme's methods and materials [1], as well as how the methods and materials have been applied in working with children with learning difficulties [2–4].

The programme is both research and evidence-based, and attempts to address reading, writing and spelling problems through activity-based learning targeting the child's specific functional areas of difficulty identified in assessment. As the programme's materials are electronic, they can be sent out by email, and there is a network of parents, teachers and therapists using the programme both locally in the SADEC region as well as internationally in the United Kingdom and in Kenya. The results have been promising, based on effective use of the materials and methods both in individual programmes involving direct physical contact as well as online.

This chapter describes how the approach to activity-based learning has been developed and implemented both prior to COVID as well as post-COVID. Pre-COVID, an activity-based approach was used based on contact sessions with supporting activities provided for implementation at home. During lockdown the activities were adapted for online work. What has been developed post-COVID is an activity-based approach to teaching reading, writing and spelling which can be implemented either through contact sessions or online. As all materials are electronic, this means that the programme can be effective in any locality in the world where parents, teachers, therapists and schools speak English, and have access to electricity and the internet.

### **2. Activity-based learning**

Our approach to activity-based learning is based on the neurolinguistic theories of the Russian neuropsychologist Alexander Luria [5–7], who suggested that human mental processes are complex functional systems that involve groups of brain areas working in concert. Each system evolves as the child develops, and makes a unique contribution to the organisation of the central processing conducted by the brain [8].

Based on the theories of Leontiev [9–11] and of Vygotsky [12–14], Luria [15] suggested that the development of higher mental functions takes place in stages. The process of learning is activity-based for the reason that the consolidation process is activity-based. It is based on increasing automaticity, in which a complex cycle of unconnected acts become a highly automatized skill. This principle applies to many different mental functions, including the neurolinguistic functioning involved in development of the ability to read fluently, to write fluently and to spell fluently [16].

#### **3. Automaticity in reading**

In terms of Luria's conceptualisation of the development of higher mental processes, the development of automaticity in reading is essential for its use in the hierarchical processing of information by the working brain. Following Luria [17], automaticity would be developed in reading when there has been sufficient practice to enable this complex functional act to become fluent enough to form the basis for higher mental processing.

Heckelman [18–19] was the first to record the use of paired reading as a method for increasing reading fluency, while LaBerge and Samuels [20] were the first researchers to focus on automaticity as a function of how reading fluency develops. Samuels [21] suggested that automaticity in reading could be trained through procedures involving repeated reading. As Samuels commented:

*"It is important to point out that repeated reading is not a method for teaching all reading skills. Rather, it is intended as a supplement in a developmental reading program. While the method is particularly suitable for students with special learning problems, it is useful for normal children as well." [22].*

The association between reading fluency and automaticity has then recurred in subsequent literature, with repeated reading being identified as effective when implemented in a variety of ways, and in a variety of different contexts. Repeated reading has been used effectively as a method for developing reading by

#### *Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

teachers [23–28], parents [29, 30], as well as peer tutors [31–35]. The evidence from these various types of implementation has been positive, effects have often been rapidly obtained, and variations in implementation procedures have produced similar positive effects (e.g. [36–45]).

Overall, automaticity has been associated with the development of both oral reading ability as well as comprehension [46–48]. Based on review and metaanalysis of the literature, the National Reading Panel [49] concluded that there was:

*"a persuasive case that repeated reading and other procedures that have students reading passages orally multiple times while receiving guidance or feedback from peers, parents, or teachers are effective in improving a variety of reading skills. It is also clear that these procedures are not particularly difficult to use; nor do they require lots of special equipment or materials, although it is uncertain how widely used they are at this time. These procedures help improve students' reading ability, at least through grade 5, and they help improve the reading of students with learning problems much later than this." [50].*

### **4. Activity-based methods for developing fluency in reading**

Wolf and Katzir-Cohen [51] have argued that as there are a number of levels of subskills and components in reading fluency instruction, there is a need for curricular strategies in dealing with fluency-based issues. They suggest that increased exploration of the subskills and components of, and issues surrounding, fluency and comprehension will contribute to understanding of both reading development as well as dyslexia subtypes.

The literature also indicates that dyslexia is best conceptualised as a spectrum which is associated with many different aspects as well as deficiencies in a number of areas of functioning [52–55]. What this implies is that reading, writing and spelling difficulties are likely to be complex, and require treatment directed at a number of variables.

For this reason a multivariate approach to fluency-based work is used in our programme. Variables affecting the child's functioning are identified through assessment. Treatment then focuses on these variables, focusing in particular on effecting change in reading fluency, writing and spelling fluency, as well as in the cognitive and metacognitive skills involved in rapid naming and sequential working memory development. Based on the variables indicated in assessment, the methods used in our programmes are individualised and activity-based. These are introduced through either contact or online sessions, or a combination of these.

Both identification of needs and implementation are thus evidence-based. Based on assessment, an individual programme is developed which targets the child's individual learning needs through focus on particular variables and their neurolinguistic underpinnings. Implementation then takes place using electronic books, activity books and materials using research and evidence-based methods. Effectiveness of treatment is monitored through ongoing evaluation.

### **5. The 3 x 3 oral impress method**

The methods used in our programme for developing reading fluency involve repetitive paired reading of sentences at foundation level, and repetitive paired reading of paragraphs once the child is able to read at a basic level. We have used an activity-based method called the 3 x 3 Oral Impress Method effectively [56, 57].

This is designed to stimulate the visual word form area in the brain identified by Dehaene [58, 59] through repetitive exposure to large-print phonically-based material, using repetitive reading and repetition of words in text to develop increased rate of reading based on increased accuracy of phonological decoding as well as lexical familiarity [60–63].

The appropriate starting point in the programme is identified through assessment. A sequence of graded written material is then used based on phonograms and rimes which are embedded in the text of a series of electronic reading fluency books, which can be either sent out by email or purchased online. Each word acts as a stimulus as the brain develops the ability to process increasingly complex phonically based reading material.

Repetitive reading of sentences is conducted at foundation level until the child's reading skills develop to the level where basic level reading material can be read by the child and a reading partner. Repetitive reading of paragraphs is then introduced, with the reading of each paragraph being repeated three times. After the first three paragraphs have been read repetitively in this way, the next three


### **Table 1.**

*The 3 x 3 Oral Impress Method.*

**Table 2.** *Model for Reading Fluency Development.* *Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

paragraphs in the story are then read repetitively, with additional repetition taking place through repeated use of words in the text of the books, on the model presented in **Table 1** above.

What this means is that the child is exposed to carefully structured repetitive use of graded reading material using large-print books in which the words and sequences of words are systematically chosen and graded [64]. The amount of text in paragraphs is also limited [65]. The books are printed using on one side of the page only. This is done for visual attentional reasons [66, 67], as well as to reduce clutter [68].

The material is then read repetitively on the model summarised in **Table 2** above, with each paragraph being read three times. In the process, the child is given the active support of a reading partner as well as the repetition of graded phonically-based words and sequences of words necessary to learn to read, and then to read fluently. As with the paired reading methods described by others [69–72], the 3 x 3 Oral Impress Method method is designed to provide an avenue through which a skilled reader (eg a parent, therapist, teacher, tutor or a reading partner) can work with a child, with both the visual cueing and voice of the skilled reader, and the phonic basis of the large-print reading material, providing the associations necessary for reading to develop.

### **6. Similarities and differences to the paired reading methods used by others**

Similar activity-based paired reading methods have been used by others with success. The 3 x 3 Oral Impress Method is similar to the methods developed by Heckelman [73, 74], in involving the child and his or her reading partner in activities involving oral reading. For this reason we follow Heckelman in using the term "impress" because paired reading is used, with the reading partner's voice being provided and heard by the child, while the child also reads out loud.

As Heckelman [75] has observed, the combining of an external voice and the child's voice provides continual active involvement in the reading process together with oral stimulation, as well as feedback on whether words are being read correctly. The child thus sees the word, hears the word and speaks the word. The child's reading partner guides the process of oral reading and repetition.

However, the 3 x 3 Oral Impress Method has the following differences to the paired reading methods used by others.


intermediate levels in the programme to repeatedly stimulate the areas of the brain involved in the reading process.


This means that our programme's fluency materials are normally used for a number of different purposes, as well as for different combinations of reading, writing and spelling activities. The level and sequence of activities is adjusted to suit the learning needs of particular children. With beginning readers, the phonic complexity and the size of unit read can be decreased and the amount of repetition increased. As fluency develops, the size of unit read and its phonic complexity can also be increased and the amount of repetition reduced. Rapid naming of numbers and words is also introduced as an integral part of the programme with the aim of developing rate and accuracy of numerical, orthographic and lexical processing [93].

The neurolinguistic model for reading fluency development used in our programme links activity, function and underlying cortical processing and has been presented in **Table 2** above. It can be summarised as follows:

At the activity level, phonically-based large print materials are used for repetitive paired reading, with the aim of developing rapid and accurate naming of individual words, and words in sequence. At the central level, the repetitive paired reading methods would involve both forward and reverse processing from the visual and occipital areas of the cortex through the different functional sections of the visual word form area to the areas of the cortex involved in phonological and language processing [60, 94].

### **7. Automaticity in writing and spelling**

Luria [95, 96] proposed that automaticity is necessary for any act (including reading, writing and spelling) to become fluent. Fluent acts then form the basis for higher level processing. Luria suggested that writing follows other mental processes in being a process which changes on a functional level, and that changes on a functional level reflect greater functional integration in the brain.

The methods used in the initial stages of our programme follow Luria in focusing on memorisation of the graphic form of each letter. We also follow Luria in developing the sounds associated with each letter as the child is exposed to the graphic form of each letter. We thus integrate the introduction of reading, writing and spelling with the introduction of phonics, with the associations developed through a sequence of activities.

The aim is that with practice, the performance on each individual element becomes altered as writing develops into what Luria has called a single "kinetic melody", in which the structures underpinning the process of writing individual

#### *Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

letters become automaticised and integrated. Similar changes also take place in other higher mental processes to which the writing process is linked [97].

What this means is that it is not only the functional structure of the processes of writing and spelling which change as automaticity develops, but also their cerebral organisation, as the activities of writing and spelling start to depend on different systems of concertedly working zones [98]. Following Vygotsky [99], this process of organisation is based on new, intermediate structures of mental processes and new interfunctional relationships which enable the performance of increasingly complex tasks by new methods [100].

Following Luria, the development and assessment of writing and spelling are linked to the development and assessment of reading ability. In our programme, automaticity is thus conceptualised as central to the development of writing and spelling, as a process which enable their development into a single "kinetic melody" [101] capable of supporting the use of reading, writing and spelling in higher mental activity.

### **8. The structured language experience approach**

For the reasons outlined in the previous section, the methods and materials used in our programme are designed to integrate the teaching of writing and spelling with the teaching of reading. The child develops writing based on use of an activity book which is phonically-based, and which introduces the associations between sounds and letters through rhyming word families.

At the foundation level, reading comprehension is taught from the outset. This is done through a process of instruction which is activity-based, using an approach called "The Structured Language Experience Approach", which is a phonicallybased teaching method in which reading, writing, and spelling are integrated with the processes of drawing and illustration [102].

The materials used at foundation level in the programme consist of a series of activity books and reading books, with a set of key words accompanying each reading book. The sequence of words in the activity book is then used to teach sound-letter associations, which are introduced through sets of rhyming words. These are used as the basis for developing sentences for purposes of reading, writing, spelling, language development and comprehension.

To avoid clutter, each page in the activity book consists of a limited number of rhyming words based on short vowel sounds, which are printed on the right hand page. The left hand page is left blank for writing and drawing. The words are then introduced as follows:


Once this has been done, the words and sentences can be typed on the laptop and illustrated with clipart. The child is assisted in this process, and the words and sentences are then printed out on plain paper to form the child's own language experience reading book.

At foundation level in the programme, the aim of the structured language experience approach is thus to extend the sets of rhyming words in the activity book through a sequence of activities based on the child's own language. This sequence of linked activities enables the rhyming words to be used in sentences which can then be copied, written, read and illustrated. Being based on the child's own language, it is then a small step for the child to be able to read the words in the sentences.

The sentences made in this way are then typed and then printed out as a language experience books. The child is also encouraged to dictate his or her own stories based on his own words and sentences, thus extending the breadth of his or her reading skills.

### **9. Phonological referencing**

At both foundation and higher levels in the programme, the activities for developing automaticity in reading, writing and spelling are based on integrating reading, writing and spelling, and follow Frith [103] and Berninger et al. [104] in stressing the need to link the evolving processes of reading and writing. They are also based on use of structured phonics, following Ehri [105, 106], who has suggested that the beginning reader/speller progresses through phases of proficiency related to his or her developing alphabetic and phonological knowledge.

The child's spelling of the rhyming words introduced in the activity books is also tested. This follows Ehri [107, 108] who suggests that orthographic learning comes about through experience with printed language, in the process of which longer and longer letter strings become stored in memory. Children in the final "consolidated alphabetic phase" are able to read fluently as well as spell accurately, by relying upon these stored orthographic representations.

Our methods for teaching spelling in the initial stages thus follow the phonologically and phonically-based stages in spelling described by Moats [109, 110]. Focus is placed on teaching through synthetic phonic approaches incorporating teaching children to isolate sounds and blend sounds into words, as well as how to create families of rhyming words based on similar phonological and phonemic elements. In addition, as the child establishes reading fluency through our foundation level and then our basic readers, our methods use activity-based learning to build the variety of phonic associations necessary to read, write and spell as follows:


*Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*


The Seven Vowel Phonic Analysis System is then worked with and applied through activities in which the letters used to represent the vowels are identified through phonological referencing. Through activity-based learning the child learns that there needs to be a vowel in every word, and that the letters a, e, i, o and u are used to represent the vowels in all positions in words, and that the use of y and w as vowels at the end of words is both logical and consistent, applying to nearly all words in English. The system thus aims to make written English as transparent as Welsh, in which the use of the seven vowels a, e, i, o, and u, as well as y and w, also applies [112, 113].

### **10. Developing phonic associations as the basis for learning to spell**

At foundation level and at reading ages up to 8 years of age, our programme would follow Moats [114, 115] in targeting phonic associations in a hierarchy in which words based on short vowel sounds would be introduced first, followed by words in which more than one letter is used to represent the vowel sounds. A set of phonic inventories is used both during initial assessment as well as during programme implementation to establish the phonic associations the child knows and does not know.

Particular phonic associations are targeted using phonogram and rime cards, as well as materials based on sets of rhyming words supported by sentences which use the rhyming words in context. Phonological referencing is then introduced on this phonically based material, focusing on how the sounds made when speaking a word orally can be mapped directly to the letters used in writing the word. Once the child has been exposed to phonological referencing using written material based on families of rhyming words, the Seven Vowel Phonic Analysis System is introduced, working with the phonically-based material in our reading fluency books.

The process of developing writing and spelling fluency is then based on a sequence of phonic analysis activities which are undertaken repetitively. The aim is to use accuracy in use of sequential working memory for words to provide the building blocks for developing fluency and automaticity in writing and spelling. The neurolinguistic model for writing and spelling fluency development links activity, function and underlying cortical processing and is summarised in **Table 3**.

The model is applied repetitively and iteratively through activity-based methods, using forward and reverse processing between oral and written language to demonstrate that "what we say is what we write." The activities involved in repetitive phonological referencing are then used as the basis for developing rapid and accurate use of working memory for individual words, and words in sequence.

**Table 3.** *Model for Writing and Spelling Fluency Development.*

### **11. The seven vowel phonic analysis system**

It will be clear from the previous section that the Seven Vowel Phonic Analysis System is an activity-based procedure for teaching how to map the combinations of letters used in writing words to the sounds made when those words are spoken orally. It focuses in particular on developing skills in word attack as well as in spelling, through focusing on the letters and letter combinations used to represent the vowel sounds in words.

Based on Oaks [116], the Seven Vowel Phonic Analysis System focuses on the vowel situation in words. Following Luria [117–119] it teaches the associations between sounds and letters repetitively, working with paragraphs drawn from the phonically-based material in our reading fluency books. As the written language in these is carefully structured and graded, it is a small step to using the material in the books for activities involving phonological referencing.

The Seven Vowel Phonic Analysis System is designed to make written English more transparent as compared to transparent orthographies such as Italian or Afrikaans or Welsh. This increases the ease with which the child can apply the universal phonic principle to the task involved in learning to read, write and spell in English [120–122]. Difficulties in developing linguistic awareness and the universal phonic principle are thus assisted, as suggested by McCutchen [123], by introducing the metacognitive strategies involved in using the Seven Vowel Phonic Analysis System, with the aim of increasing the consistency with which the letters used to

### *Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

#### **Table 4.**

*Model for Development of Sequential Working Memory for Words.*

represent the vowel sounds in the English language can be mapped back to the sounds made when words are spoken orally [124].

This is done through an activity-based method based on five steps:

Step One: A descriptive paragraph from the child's reading fluency book is copied into the child's writing book.

Step Two: The letters representing the vowels in each word are identified by placing the hand under the chin and then underlined in colour.

Step Three: Target words (defined as words in which more than one letter is used to represent the vowel sounds) are then analysed using phonological referencing.

Step Four: The target words are then written, occluded by being covered with the non-dominant hand, and then written again from memory.

Step Five: The sequence of words in the sentences in the paragraph is revisualised and then tested in sequence through dictation.

This five step procedure is then used repetitively, with the aim of providing the phonic analysis and sequential working memory skills necessary for the development of writing and spelling fluency. This is done through a sequence of activities designed to develop the ability to use phonologically-based, phonically-based and visually-based sequential working memory skills.

The sequence of activities is called "targeted revisualisation". It is called "targeted" as the learning process focuses on target words (words in which more than one letter is used to represent the vowel sounds), which are then analysed using the Seven Vowel Phonic Analysis System and then learned using computerbased visualisation techniques. The process of targeted revisualisation involves the child in revisualising individual words, by first speaking the sequence of letters seen in the mind while visualising the words, and then using sequential working memory to write both individual words and sequences of words from memory.

How to do this has been presented in **Table 4** above, and is described in the next section.

### **12. The targeted analysis, revisualisation and sequential spelling programme (TARSP)**

The Targeted Analysis, Revisualisation and Sequential Spelling Programme [125] is based on indications from the literature that even in pictographic written language systems like Chinese, children learn to read using phonic strategies

[126, 127]. In introducing the Targeted Analysis, Revisualisation and Sequential Spelling Programme, the child is taught through combining phonic analysis and revisualisation in activities designed to develop sequential working memory for words.

This is done as follows:

Step One: The child uses the Seven Vowel Phonic Analysis System to identify the target words (words in which more than one letter is used to represent vowel sound) from a graded written paragraph, and lists these in his or her writing book.

Step Two: The child types the target words on the laptop and uses phonological referencing to identify and then colour code the letters used to represent the vowel sounds in each target word.

Step Three: The child is taught how to revisualise the target words using a combination of phonic analysis and mental imagery.

Step Four: The accuracy with which the child remembers the target words is then tested by writing the individual words from memory.

Step Five: The child is taught how to use sequential working memory to recall the form and structure of the words in the paragraph in sequence. This is done by teaching the child how to revisualise and recall the sequences of letters used both in the individual words, as well as how to revisualise and recall the sequences of words used in sentences and paragraphs.

Step Six: Errors made by the child in writing the words and sequences of words from memory are identified. Phonic associations are taught using phonograms and rimes. The error words are then phonologically referenced, learned through occlusion and used in written sentences.

The process of targeted revisualisation thus involves work in four areas (phonic analysis based on phonological referencing, revisualisation, developing sequential working memory for words, and systematic phonic instruction targeting the spelling errors made by the child). These are linked through a sequence of activities, each of which plays an integral part in developing accuracy in use of sequential working memory, as described in the section following.

#### **13. Developing sequential working memory for words**

The aim of the Targeted Analysis, Revisualisation and Sequential Spelling Programme is to develop sequential memory for written words, based on the evidence of a common linguistic awareness manifesting in phonological, orthographic, and morphological awareness as suggested by Berninger et al. [128, 129]. It applies phonic principles in analysing and recalling words in sequence, based on the evidence of a universal phonic principle manifesting across different orthographies as suggested by Perfetti, Zhang and Berent [130] (1992). Following McCutchen [131], the Targeted Analysis Revisualisation and Sequential Spelling Programme aims develop linguistic awareness through the metacognitive strategies involved in phonological referencing, as the basis for developing sequential working memory for words.

The process of targeted revisualisation is based on a sequence of visually cued phonic analysis and phonological referencing activities which are undertaken repetitively. The aim is to develop accuracy in use of sequential working memory for words to provide the building blocks for developing fluency and automaticity in writing and spelling. This is done through repetitive activities undertaken in four stages, as follows (**Table 5**).


*Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

#### **Table 5.**

*Summary of Stages and Focuses of Mediation in the Targeted Analysis, Revisualisation and Sequential Spelling Programme.*

On a phonological and phonic level, the model is based on the coding and recoding of phonic associations through activities in which the child writes, types and colour codes the vowels in words, by underlining the letters used to represent the vowel sounds in colour as well as using the colour coding feature in a word processing programme. On a visual level, the model is designed to make the letters used to represent the vowel sounds in words stand out in colour.

As this occurs, both the phonic associations and visual contrasts used to identify the letters representing the vowel sounds in words are used to develop working memory for words as well as sequential working memory. Fluency in writing and spelling is then based on increasing automaticity in recalling the sequences of letters used in individual words, the sequences of words used in sentences, and the sequences of sentences used in paragraphs. Spelling errors made by the child are retaught using methods based on phonological referencing, occlusion and use of an electronic tachistoscope.

### **14. Implementation and results pre COVID**

Pre COVID, our methods and materials for developing automaticity in reading, writing and spelling were implemented over a number of years through contact

sessions in my practice, as well as by a network of other therapists, teachers and parents using our methods and materials. From first interventions using large-print phonically based materials in the 1990's to the date of lockdown in our country in March 2020, positive results were obtained which have been presented in a number of previous publications on the programme [132–135]. These can be accessed online by clicking on the links in these references at the end of this chapter.

Based on aggregation of the individual case study analyses of results obtained through use of the programme's materials and methods, longitudinal trends in the data indicated that the following variables influenced successful implementation of the programme over an eight year period:


Where the above variables have applied, results post-COVID have also been consistently good, based on contact implementation, online implementation as well as implementation strategies involving combinations of online and contact implementation. These are described in the section following.

### **15. Strategies for programme implementation post COVID**

COVID lockdown presented a number of challenges, but also provided a number of opportunities. Many of the challenges stemmed from my own decision to discontinue contact sessions in my practice until such time as a vaccine became available. This led to a variety of additional strategies for implementing our materials and methods, using formats involving online sessions supported by additional home-based sessions for children.

Examples of the types of format used are presented in **Tables 6** and **7** below.

The formats enabled parents and tutors to work with children using a variety of different types of activities linked to materials which were delivered by email over the lockdown period. This then provided opportunities for extending the range of online services provided by my practice as well as the extent of materials, methods and assessment tools used for online work.

As the schools also moved to online work, each child's programme was individually designed to develop the basic skills necessary to be able to complete the assignments being set by the child's classroom teacher. Both the programme provided by the child's school and our own programme activities would then be supported by either the child's parent or a tutor.

It thus became possible to provide an activity-based individual programme for the child drawing on the following types of materials from my practice's data-base:



*Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

*Add:*

*Daily repetitive paired reading on basic evel reading fluency book.*

*Daily work in Level One phonic workbook or in Level Six foundation level activity book.*

*Daily maths activities on maths website.*

#### **Table 6.**

*Activity-based Learning Format Designed for Work with an 8 year old child. Learning Cycle Eleven implemented on July 18th 2020*


Post COVID, feedback on how the child has coped with each type of activity is provided by photographs sent by email or WhatsApp, enabling the next format in the child's programme to be evidence-based, linked to ongoing evaluation of learning needs. Assessment is then built into programme implementation at regular intervals. The model used for implementation is action research based, and is presented in **Table 8** on the next page.

As the practice's data-base is extensive, the planning and implementation model summarised in **Table 8** implies that each child's programme can be multivariate, addressing a number of different learning needs through use of a variety of graded activities. The programme is then implemented using online sessions supported by learning materials provided by email. The aim is that programme implementation can take place with support from parents or tutors, working with a variety of electronic materials made accessible online via links to websites, or delivered by email. Methods used in the programme are documented in illustrated implementer manuals, and are demonstrated working online, supported by cellphone and email contact.



*Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

*Add:*

*Daily repetitive paired reading on intermediate level reading fluency book.*

*6 word a day long term memory spelling programme (6 words a day times three days, learn and test the fourth day, learn any errors using occlusion and use error words in sentences). Use tachistoscope for rapid naming and writing based on use of short-term visual memory.*

#### **Table 7.**

*Activity-based Learning Format Designed for Work with a 12 year old child: Learning Cycle Five implemented 29th April 2020*

**Table 8.**

*Action Research Cycle for Planning and Implementation of Activity-based Online Programmes.*

### **16. Changes in assessment and training post COVID**

COVID has also presented challenges and opportunities in both assessment and training in programme use. Pre COVID, assessment was undertaken through contact sessions, through a number of tests administered across the table. Programme implementation then took place working with therapists, teachers, tutors and parents who were trained in programme implementation through a mediated training programme.

Post COVID, the programme has developed assessment strategies involving combinations of contact and online work. Contact testing is conducted both locally and internationally, working in association with other therapists located in areas close to where children live. This is then supplemented by online testing, with the aim of developing an individual programme relevant to the child learning needs, at a level of language, reading, writing and spelling appropriate to the child's basic skills, and varied in terms of the child's individual needs.

Implementation then takes place through online sessions supported by learning formats based on materials and methods drawn from the electronic data base, with each activity focused on the child's learning needs. Training is provided to programme users as an integral part of the process. Manuals are provided to assist parents, teachers, therapists and tutors in implementing the reading, writing, spelling and working memory activities which form the basis of each child's programme. Additional support is also provided to programme users through training materials, as well as through sessions conducted online with the therapists, teachers and parents who work in association with my practice.

A number of parents, therapists and teachers are currently working with the practice's methods and materials, both in the SADEC countries as well as in the UK and East Africa. What has developed in response to COVID are combinations of contact and online assessment and training. With increased use of online technology, it has been possible to plan sessions and work online with others using the programme's methods and materials, and in the process to demonstrate which activities work best, how to implement activity-based learning using the programme's methods and materials, and exactly what to do step by step. This has led to forms of shared planning and implementation, supported by electronic materials and manuals.

These are exciting developments in which there are many possibilities for work in different geographical areas of our own country, which is culturally, linguistically and socio-economically diverse, but which uses English as the basis for schooling, commerce and the market-place. It has also led to both interest and implementation possibilities in other countries in which English is spoken and used as the basis for work in schools, as well as more broadly in society.

### **17. Summary and evaluation**

The reading, writing and spelling fluency programmes described in this chapter are activity-based, and are introduced through online sessions related to the child's individual learning needs as identified through initial assessment and ongoing evaluation. Based on evidence provided by testing, an individual programme is developed for the child based on areas of need. Electronic books, activity books and activity-based materials are then used to develop automaticity in reading, and automaticity in writing and spelling, as well as to focus on linked difficulties in phonological and phonemic development, rapid naming and working memory development.

The methods and materials described in this chapter can be used as a response to dyslexia as well as for work with children whose skills in reading, writing and spelling are not well developed. Reading fluency is initially targeted, together with the development of phonic associations based on use of phonogram and rime cards as well as rhyming word families. Once observable differences in reading fluency are noted, reading comprehension activities are introduced together with visually cued phonic analysis based on phonological referencing methods, which are used as the basis for developing writing and spelling fluency.

Following Jorm and Share [136–140], the phonological referencing methods used in a child's programme are based on the teaching of skills for phonological recoding (print-to-sound translation, as well as translation of sound back to print). Phonic analysis and revisualisation are then used in combination to develop the detailed orthographic representations necessary for fast, efficient visual word recognition, as well as the detailed orthographic representations necessary to spell both individual words and words in sequence.

### *Activity-Based Online Learning: A Response to Dyslexia and COVID DOI: http://dx.doi.org/10.5772/intechopen.96359*

The methods used in our fluency-based programmes are thus multivariate, based on use of a combination of repetitive paired reading, repetitive phonological referencing, as well as the training of rapid naming and sequential working memory skills. The evidence from aggregated case studies of children who have worked with a combination of the methods described in this chapter indicates that there are benefits in improvement in reading, spelling individual words and spelling words in sequence, with backwash effects occurring across these areas. Case contrasts indicate lessened effects from programme implementation where there has been systematic variation in either the implementation of repetitive paired reading or repetitive phonological referencing using the methods described in this chapter, and in previous chapters on the programme [141–144].

Post COVID, both contact and online implementation have been undertaken in which materials from the practice's data-base are used in interactive sessions with children, with supporting manuals and training materials delivered to users by email. Each child's programme is then supported with formats designed to provide an activity-based learning programme focused on the child's learning needs. Training can also be provided to users interactively and step by step, with methods demonstrated either through contact or online, supported by implementation material, training material and illustrated manuals.

This hybrid assessment, training and implementation model has evolved as a response to the needs for social distancing required by COVID. The interventions with each child can be flexible as well as multivariate, and can be provided both locally and internationally wherever the internet and email are available. Both results and user evaluations are positive, indicating that there are a number of possibilities for post COVID implementation of the programme with children with reading, writing and spelling difficulties, and as well as with therapists, teachers and parents working with dyslexic children in different geographic areas and different countries.

### **Author details**

Charles Potter Psychologist in Private Practice, Johannesburg, South Africa

\*Address all correspondence to: pottercs@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[135] Potter, C.S. (2019). Training Reading, Writing and Spelling Fluency: Centre-Periphery Dissemination through Interactive Multimedia. In D. Cvetkovic (Ed.)., *Interactive Multimedia - Multimedia Production and Digital Storytelling.* IntechOpen, DOI: 10.5772/ intechopen.82812. Available from: https://www.intechopen.com/books/ interactive-multimedia-multimediaproduction-and-digital-storytelling/

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[138] Share, D. L. (1995). Phonological recoding and self-teaching: Sine qua non of reading acquisition. *Cognition*, 55 (2), 151–218.

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[140] Share, D. L. (2004). Orthographic learning at a glance: On the time course and developmental onset of selfteaching. *Journal of Experimental Child Psychology*, *87*(4), 267–298.

[141] Potter, C.S. (2015) Using Phonically Based E-books to Develop Reading Fluency. In B. Gradinarova (Ed.), *E-Learning - Instructional Design, Organisational Strategy and Management.* Rijeka: InTech, DOI: 10.5772/61607. Available from: http://www.intechopen. com/books/e-learning-instructional-de sign-organizational-strategy-and-ma nagement/using-phonically-based-ebooks-to-develop-reading-fluency

[142] Potter, C.S. (2017a). Developing Automaticity in Children with Learning Disabilities: A Functional Perspective Part One: Theory and Assessment. In C. Ryan (Ed.). *Learning Disabilities*. London: InTech, Available from: https:// www.intechopen.com/books/learningdisabilities-an-international-perspec tive/developing-automaticity-in-childrenwith-learning-disabilities-a-functionalperspective-part-one-the

[143] Potter, C.S. (2017b). Developing Automaticity in Children with Learning Disabilities: A Functional Perspective Part Two: Programme Methods and Materials. In C. Ryan (Ed.). *Learning Disabilities*. London: InTech, Available from: http://www.intechopen.com/artic les/show/title/developing-automaticityin-children-with-learning-disabilities-afunctional-perspective-part-two-pro

[144] Potter, C.S. (2019). Training Reading, Writing and Spelling Fluency: Centre-Periphery Dissemination through Interactive Multimedia. In D. Cvetkovic (Ed.)., *Interactive Multimedia - Multimedia Production and Digital Storytelling.* IntechOpen, DOI: 10.5772/ intechopen.82812. Available from: https://www.intechopen.com/books/ interactive-multimedia-multimediaproduction-and-digital-storytelling/ training-reading-writing-and-spellingfluency-centre-periphery-dissemina tion-through-interactive-mul

## **Chapter 6** Artificial Intelligence in Education

*Andrej Flogie and Boris Aberšek*

## **Abstract**

Information technology, through networking, knowledge-based systems and artificial intelligence, interactive multimedia, and other technologies, plays an increasingly important role, which will even increase in the future, in the way that education is taught and delivered to the student. For this reason, we decided to present some ideas for such learning-training environments in education in this chapter. Like many researchers in other countries, we are also developing a userfriendly general system, designed particularly for solving problems. It is based on experience-based intelligent tutoring systems, and intended primarily for executing better lessons and for students' self-learning. Like all powerful tools, experiencebased AI design approaches must be applied carefully. Without a carefully designed experience and extensive testing, these systems could easily result in unwanted outcomes (such as negative training or increased phobia anxiety). Despite the promise of the early efforts, the best approaches to designing these experiences are still topics of research and debate. Any technology as powerful as AI provokes many general social and ethical questions in all of us. Does AI make killing by remote control too consequence-free? Do AI models systematize existing biases? What will AI do when it enters education? We will try to provide an answer to this question in the following chapter.

**Keywords:** artificial intelligence (AI), education, machine ethics, machine behavior, intelligent tutoring system

### **1. Introduction**

*"Natural science is knowledge about natural objects and phenomena. We ask whether there cannot also be 'artificial' science – knowledge about artificial objects and phenomena."*

*Herbert Simon*

*Teachers will not be replaced by technology, but teachers who do not use technology will be replaced by those who do.*

*—Hari Krishna Arya*

For years, experts have warned against the unanticipated effects of general artificial intelligence (AI) on society [1, 2], predicting that by 2029 intelligent machines will be able to outsmart human beings. Stephen Hawking argues that *"once humans develop full AI; it will take off on its own and redesign itself at an ever-increasing rate*". Elon Musk warns that AI may constitute a "fundamental risk to the existence of human civilization". If the problems of incorporating AI in manufacture and service operations, i.e. using *smart machines*, are smaller, as the 'faults' can be recognized relatively quickly and they do not have a drastic effect on society, then the *incorporation of AI in society and especially in the educational process* is an extremely risky business that requires a thorough consideration. The consequences of mistakes in this endeavor could be catastrophic and long-term, as the results can be seen only after many years. The threat of AI and its potential evolving into a commonly named 'superintelligence' can be summarized with the following thought of Boston:

"/…/ I try to understand the challenge presented by the prospect of superintelligence, and how we might best respond. This is quite possibly the most important and most daunting challenge humanity has ever faced. And – whether we succeed or fail – it is probably the last challenge we will ever face." [3].

The scientific background of such ideas and questions is based on the findings that have materialized at the intersection of the fields of philosophy (ethics), artificial intelligence, and pedagogy (education). Our research will stem from the findings of authors such as Turing, Bostrom, Rahwan, Kurzweil and others. Also, this idea is based on the European AI Alliance, which the European Commission launched at the beginning of 2018. The main documents are "*Artificial Intelligence for Europe*<sup>1</sup> [4] and "*Coordinated Plan on Artificial Intelligence* [5]."

The increasingly faster hardware and the progressively optimized software in the realm of computers have, during the course of the past few years, stimulated and disturbed academic philosophy, which quickly began pointing out the ethical issues that might arise with the usage of artificial intelligence. Saying that such problems are a thing of the distant future is not something philosophers and researchers of AI would agree with. AI expert Nick Bostrom (University of Oxford) offers the following answer to the question of "When will human-level machine intelligence (HLMI) be attained?": "10% probability of HLMI by 2022, 50% probability by 2040, and 90% probability by 2075" [3].

### **1.1 Why must we be careful?**

Let us start at the beginning of the story about the easy and the hard philosophical problem of incorporating AI. If the problems of incorporating AI in manufacture and service operations, i.e. using *smart machines*, are smaller (hence the name, easy problem), as the 'faults' can be recognized relatively quickly and they do not have a drastic effect on society, then the *incorporation of AI in society and especially in the educational process* (hence the name, hard problem) is an extremely risky business that requires thorough consideration. The consequences of mistakes in this endeavor could be catastrophic and long-term, as the results will be seen only after many years.

AI is ultimately only a computer program, a "simple" optimization algorithm. Such algorithms can contain different ethical constraints (law) in the source code. A well-known historical example in the form of such simple "robotic laws" dates as far back as 1950, when Isaac Asimov proposed the following:


<sup>1</sup> http://www.ijcai-18.org/wp-content/uploads/2018/07/1\_20180717\_IJCAI\_ECAI\_Cecile-Huet.pdf.

### *Artificial Intelligence in Education DOI: http://dx.doi.org/10.5772/intechopen.96498*

It is clear from these laws that the robot (intelligent machine), or, in today's terminology, AI, must protect humans and put the safety of human beings before its own existence. 50 years later, however, Mark W. Tilden wrote similar, but at the same time different laws2 :


Tilden's laws suggest that the primary role of the robot (AI) is first and foremost to protect itself from the outside world, including human beings. Because the AI of today learns primarily from the world wide web, where both types of laws can be found, an ethical dilemma could thus be created: *which of these two sets of laws should be considered as guidelines, or, in other words, what is the Categorical Imperative for AI according to Kant* [7]*?*

### **1.2 Machine ethics and/or machine behavior**

Machine morality in intelligent systems, whether physical systems with a mind and body or just thinking algorithms somewhere in the cloud, is a recurring issue. Morals demonstrate the relationship of humanity to nature and society and are manifested as a sum of values (rules, norms, principles, categories, ideals, etc.), according to which we make decisions, what is good and what is bad, what is just and what is unjust, what is right and what is wrong, and in line with which we also behave. When it comes to the morality of smart machines, philosophers mostly focus on theoretical questions such as: *does AI have the status of a moral agent, is AI responsible for its actions, is AI a 'being' with a higher moral status*, etc. – rather than on such a specific and practical area as is the usage of AI in education, especially in the field of ensuring social competences and developing emotional intelligence [8, 9].

The ethical dilemma related to the understanding and interpretability of the behavior of AI agents, is one of the pivotal challenges of the next decade of AI. Until today, most of the interpretability techniques have focused on exploring the internal structure of deep neural networks. But *machine behavior* [10] relies more on observations than on engineering knowledge in order to understand the behavior of AI agents. Most of the conclusions obtained from observations in nature are not related to knowledge from biology, but rather to our understanding of social interactions. In the case of AI, scientists who study the behaviors of different virtual and embodied AI agents are predominantly the same scientists who have created the agents themselves. But understanding AI agents must go beyond interpreting a specific algorithm and requires analyzing the interactions between agents and with the surrounding environment. In order to accomplish that, behavioral analysis via simple observations can be used as a powerful tool.

### **1.3 Machine behavior**

Machine behavior [10] is a field that leverages behavioral sciences to understand the behavior of AI agents. Currently, scientists who most commonly study the behavior of machines are computer scientists, roboticists and engineers who have

<sup>2</sup> http://www.botmag.com/the-evolution-of-a-roboticist-mark-tilden/

created the machines in the first place, but they are typically not trained behaviorists. Similarly, even though behavioral scientists understand those disciplines, they lack the expertise to understand the efficiency of a specific algorithm or technique. From that perspective, machine behavior sits at the intersection of computer science, engineering, and behavioral sciences, in order to achieve a holistic understanding of the behavior of AI agents. As AI agents become more sophisticated, analyzing their behavior is going to be a combination of understanding their internal architecture (the domain of computer scientists), as well as their interaction with other agents and their environment (the domain of behavioral scientists). While the former aspect will be a function of deep learning optimization techniques, the latter will rely partially on behavioral sciences.

In developing a new transdisciplinary science, which we call *AI behavioral science,* we, as many others researchers, use Nikolaas Tinbergen's work [11] for identifying the key dimensions of animal behavior. Tinbergen's thesis was that there were four complementary dimensions to understand animal and human behavior:


Despite fundamental differences between AI and animals, machine behavior borrows some of Tinbergen's ideas to outline the main types of behavior in AI agents. Machines have *mechanisms* that produce behavior, undergo *development* that integrates environmental information into behavior, produce *functional consequences* that cause specific machines to become more or less common in specific environments, and embody *evolutionary histories* through which past environments and human decisions continue to influence machine behavior. An adaptation of Tinbergen's framework to machine behavior is schematically presented in **Figure 1**.

Four Tinbergen's dimensions [11] provide a holistic model for understanding the behavior of AI agents. However, these four dimensions do not apply in the same way with respect to whether we are evaluating a classification model with a single agent, or with hundreds of agents. In that sense, machine behavior applies the previously mentioned four dimensions across three different scales:

1.The first is **Individual Machine Behavior:** this dimension of machine behavior attempts to study the behavior of individual AI agents by themselves. There are two general approaches to the study of individual AI agent behavior. The first focuses on profiling the set of behaviors of any specific machine agent using a within-machine approach, comparing the behavior of a particular machine across different conditions. The second, a betweenmachine approach, examines how a variety of individual machine agents behave in the same conditions [12].


#### **Figure 1.**

*Tinbergen proposed that the study of animal behavior can be adapted to the study of machine behavior [10, 11].*


### **2. Solutions, or: Why should we be optimistic?**

What can be done? In trying to provide a solution, a simple example related to the notion of *proprioception* [12] can be considered*.* What does proprioception really mean? Proprioception could also be called *self-perception of thought*, or *selfawareness of thought*, i.e., thought, which is able to perceive its own flow, be aware of its own movement.

With proprioception, the emotional intelligence (EI) of a person (**Figure 2a**) also develops, which will change, step by step, the human historical memory, and add new elements to this historical memory on the level of intuitive thinking. By way of analogy, we can develop a similar philosophy of proprioception for AI (**Figure 2b**). We must therefore develop this awareness in every individual - human or AI; we must "change" or establish the specific way of thinking (creative, critical, and conscious thinking); and it is very important to begin this process with agents (human or AI) of the "youngest" possible age. These competences must be developed step by step, which will enable us to deal with the day-to-day needs of others, and help raise the awareness. This transformation/analogy is shown schematically in **Figure 2**.

#### **2.1 Machine behavior and education**

Before any kind of learning environment is given some sort of intelligence (see **Figure 3**), machine ethics and/or machine behavior must be built into this learning

#### **Figure 2.**

*From human to AI emotional intelligence (EI).*

#### **Figure 3.**

*Moral and ethical dilemmas in society and education.*

environment, in order to ensure that the cognitive, social, and emotional competences of students are defined in a way that will allow them to be formalized or translated into a scientific language, into a language familiar to the machine.

Additionally, methods have to be defined for assessing whether such intelligent systems work correctly in the long-term, since either noticing or removing

#### *Artificial Intelligence in Education DOI: http://dx.doi.org/10.5772/intechopen.96498*

the consequences which their failure or irregular operations have on the moral development of individuals, is not possible in real time. And since these methods, as mentioned earlier, are not in the domain of computer scientists, roboticists and engineers who have created the machines, but rather in the hands of experts from the field of behavioral science, the roles of the evaluator and the auditor must take over the role of teachers. For this reason, teachers must be able to acquire some kind of knowledge from the area of AI behavioral science in order to become competent observers and evaluators of such intelligent learning environments [13].

Additionally, methods have to be defined for assessing whether such intelligent systems work correctly in the long-term, since either noticing or removing the consequences which their failure or irregular operations have on the moral development of individuals, is not possible in real time. And since these methods, as mentioned earlier, are not in the domain of computer scientists, roboticists and engineers who have created the machines, but rather in the hands of experts from the field of behavioral science, the roles of the evaluator and the auditor must take over the role of teachers. For this reason, teachers must be able to acquire some kind of knowledge from the area of AI behavioral science in order to become competent observers and evaluators of such intelligent learning environments [13].

The general question to be answered could therefore be formulated thus: *"What are the moral problems of using advanced learning systems and modern learning environments supported by AI methods*?", with the concrete goal of the research being *the development of a test, on the basis of which teachers could assess whether an intelligent accessory (program or algorithm) for learning is such that it ensures the acquisition of all cognitive, social, and emotional competences in students*, i.e., whether it is 'safe' to use in the educational process. The development of such a test, as well as the related knowledge and skills, could encourage the development of various other similar 'security' tests for AI usage in other areas.

### **2.2 From smart to intelligent self-learning tutoring systems**

Learning, knowledge and intelligence are closely related. Although there is no universally accepted definition of intelligence, it can be roughly defined as follows:

#### *Intelligence is the ability to adapt to the environment and to solve problems.*

Nowadays, most researchers agree that there is no intelligence without learning, so learning adaptation takes place in almost all living beings, most obviously in humans. Learning by a living system is called *natural learning*; if, however, the learner is a machine – a computer, it is called *machine learning*. The purpose of developing machine learning methods is, besides better understanding of natural learning and intelligence, to enable algorithmic problem-solving that requires specific knowledge. In order to solve problems we obviously need knowledge and the ability to use it. Often such knowledge is unknown or is used by a limited number of human experts. Under certain preconditions, by using machine learning algorithms, we can efficiently generate knowledge which can be used to solve new problems.

Even the whole natural evolution can be regarded as learning: through genetic crossovers, mutation and natural selection, it creates ever better systems, which are capable of adapting to different environments. The principle of evolution can also be used in machine learning to guide the search in the hypothesis space through the so called *genetic algorithms*.

### **2.3 Artificial intelligence and learning**

A long-term goal of machine learning research, which currently seems unreachable, is to create an artificial system that could achieve or even surpass human intelligence. A wider research area with the same ultimate goal is called *artificial intelligence*. Artificial intelligence (AI) research deals with the development of systems that act more or less intelligently and are able to solve relatively hard problems. These methods are often based on imitation of human problem solving. AI areas, besides machine learning, are knowledge representation, natural language understanding, automatic reasoning and theorem proving, logic programming, qualitative modeling, expert systems, game playing, heuristic problem solving, artificial senses, robotics and cognitive modeling.

Machine learning algorithms play an essential role in all AI areas. One has to include learning practically everywhere. By using learning techniques, the systems can learn and improve in perception, language understanding, reasoning and theorem proving, heuristic problem solving, and game playing. The area of logic programming is also highly related to inductive logic programming that aims to develop logic programs from examples of the target relation. Also in qualitative modeling the machine learning algorithms are used to generate descriptions of complex models from examples of the target system behavior. For the development of an expert system one can use machine learning to generate the knowledge base from training examples of solved problems. Intelligent robots inevitably have to improve their procedures for problem solving through learning. Finally, cognitive modeling is practically impossible without taking into account learning algorithms.

#### *2.3.1 Natural learning*

Humans learn throughout our whole lives. We learn practically every day, which means that our knowledge is changing, broadening and improving all the time. Just like humans, animals too are capable of learning. The ability to learn depends on the evaluative stage of species. Investigation and interpretation of natural learning is the domain of *the psychology of learning* and *educational psychology*. The former investigates and analyses the principles and abilities of learning. On the other hand, the latter investigates the methods of human learning and education and aims at improving the results of educational processes. Educational psychology considers attention, tiredness and motivation to be of crucial importance for a successful educational process and carefully takes into account the relation between the teacher and the students, and suggests various motivation and rewarding strategies. All those are of great importance for human learning, however, they are much less important for (contemporary) machine learning.

### *2.3.2 Learning, intelligence, consciousness*

As we already stated, intelligence is defined as *the ability to adapt to the environment and to solve problems*. Learning alone, however, is not enough. In order to be able to learn, a system has to have some capacities, such as sufficient memory capacity, ability to reason (processor), ability to perceive (input and output), etc. These abilities do not suffice if they are not appropriately integrated or if they lack an appropriate learning algorithm. In addition, efficient learning also requires some initial knowledge – background knowledge, which is inherited in living systems. Through learning, the abilities of the system increase, and therefore the intelligence of the system also increases [14].

### *2.3.3 The amount of intelligence*

Systems cannot be strictly ordered with respect to the amount of intelligence, because we have to consider various types of intelligence (abilities): numerical, textual, semantical, pictorial, spatial, motor, memorial, perceptive, inductive, deductive, etc. Lately, even emotional intelligence became widely recognized. Some authors describe more than a hundred types of human intelligence. A system (human or machine) can be better in some types of intelligence and worse in others, and vice versa. When speaking about artificial intelligence, we do not expect an intelligent system to be extremely capable in only one narrow aspect of intelligence, such as for example the speed or the amount of memory, the speed of computation or the speed of searching the space or (almost optimal) game playing. The computers of today already have very advanced capabilities in each of these aspects. We expect an intelligent system to be (at least to some extent) intelligent in *all* areas which are characteristic of human problem solving. It seems that we need an integration of all different types of intelligence into a single sensible whole (a kind of supervisory system), so that during problem solving it is possible to switch appropriately between different types of intelligence. Anyway, most of the speculations about artificial intelligence do not take into account yet another level: consciousness (which seems to be a good candidate for the supervisory system).

### **3. Education 4.0 in society 5.0**

The use of contemporary learning strategies, such as games, research-based and problem-based learning connected to collaborative teaching/learning, and brain-based techniques based on cybernetics theory and information-communication technologies, have provided scholars from diverse disciplines with an unusual opportunity to observe possible flaws in their own thinking [12, 15, 16]. The choice of method was crucial: if we were to report results obtained only through conventional, standard behavioristic methods, our work would have been less noteworthy, less critical, and less memorable. This is why we did not choose demonstrations over standard methods, because we wanted to influence the entire spectrum of audiences. We preferred *problem-based and research-based methods* and *collaborative learning,* because they were more fun for students, and we were lucky in our choice of method, as well as in many other ways. We used the *brain-based technique* because it provides the educator with an understanding of what happened, and of how to react during the lecture. And we proposed intelligent serious games and game-based learning because they increase motivation.

The spontaneous search for intuitive solutions to complex problems, such as for example ecological problems, or today's global pandemic problem, sometimes fails – neither an expert solution, nor a heuristic answer comes to mind. The responsibility of the teacher is to equally develop all ways of problem solving, critical thinking, and decision-making, by choosing appropriate research problems and using a transdisciplinary model of teaching [15].

There is a huge number of opportunities to introduce novelties like the proposed problem- and research-based learning in the learning process simply by being creative; for instance, the teacher can use fresh examples or problems, or surprise students with new data, or present a scenario that is completely unpredictable. The teacher can also engage students through games and simulations that require them to apply the information in unfamiliar contexts. E-learning environments, role play, energizing online discussions, and quick serious games, can all add sensory stimuli to raise the blood pressure and epinephrine levels to eliminate drowsiness, reduce restlessness, and reinforce information. Allowing learners to do some research and

exercises on their own to better understand abstract ideas, write an essay, or work with an interactive simulation, are also helpful strategies.

### **3.1 Cybernetics, learning and AI**

The purpose of this chapter is to complement the preceding ones, changing the focus from the dynamics of social systems to that of individual human systems and developing their emotional intelligence (EI). It will be seen that second-order cybernetic 4.0 systems study self-observing systems, which are comprised by *cognitive machines*, information processing mechanisms that reside in the human mind [17].

The idea of rationality as a *cognitive machine* that has as its purpose though coherence will be offered as the staple of second-order cybernetics 4.0; another aspect of it will be that of heuristics, not only as practical reasoning but as ways of conceiving and understanding the world. The framework that will be offered will consist of the understanding of patterns (order), their proportion (balance) and harmony as their functional conjunction; constructive epistemology will also be delved upon in order to create a complete perspective upon human psychic systems.

Finally, the ideas of second-order cybernetics 4.0 will culminate in the idea of social and cognitive morphogenesis as heuristics is related to measures of complexity: order will be related to hierarchy, balance to self-similarity and harmony to universality; it will be concluded that repetition is the most adequate measure of complexity in social systems.

### **3.2 Brief description of second-order 4.0 cybernetic pedagogy**

### *3.2.1 Points of contact between second-order and second-order 4.0 cybernetics*

Self-consciousness is the point of transition between lower cognition (knowledge and lower levels of cognition without any emotional intelligence (EI) components which pertain to second-order cybernetics) and that which belongs to human beings, i.e. high cognition (developing cognitive and social competences, which is the object of study of what will be called second-order 4.0 cybernetics). The latter is referred to as a high cognition model because of the self-consciousness that a system can acquire through self-observation, and thus become teleonomical and teleological. Before entering the study of second-order 4.0 cybernetics, it is necessary to further develop the notion of cognition, so that the analysis will be complete.

Let us start with the pioneer of the psychological theory of cognitive development and learning, Piaget. In his widely known psychological research, Piaget makes a typology of the cognitive development of a human being from birth to adulthood:


manipulation of symbols related to concrete objects; operational thinking predominates.

• *Formal operational stage* (adolescence to adulthood): abstract intellectual operations appear, personality forms and there is an affective and intellectual insertion into adult society.

If we disregard that children over the past few decades have been growing up in significantly different circumstances, and have developed differently on account of an increased access to information, we can still use Piaget's findings as the starting point for the further development of second-order 4.0 cybernetics pedagogy.

Every state is distinguished from the preceding one because of the appearance of new original cognitive structures. In this typology, the difference between lower and higher cognition can be seen more clearly: while in the sensorimotor there is motor activity, knowledge based on experience and interaction and limited language acquisition, in the formal operational stage an individual can communicate with others by means of a symbol system, they are capable of logical and abstract reasoning and start to develop their emotional intelligence. This is the transition between lower cognition in animals and primates who possess it alongside a limited ability for self-observation, and human beings, which are capable of higher cognition by means of language, abstraction and formal reasoning. Higher cognition has already been defined, but a reprisal of the concept is useful: it is the processing (storage, retrieval, transformation, creation and transmission) of information made by an autopoietic system in its interaction with what surrounds it (environment and other beings) with the possibility of stating a purpose beyond self-sustainment.

### *3.2.2 Second-order 4.0 cybernetic pedagogy as the realm of self-observing systems*

Second-order 4.0 cybernetic pedagogy exhibits features of both first- and second-order cybernetic machines. Second-order 4.0 cybernetics studies cognitive machines (in our case the tutoring system as a universal meta-model), information processing mechanisms of the high order that have their basis within the neural network of human beings, that is, it is the cybernetics of human beings transforming the human being's reaction and/or activities in AI form, to build intelligent tutoring systems (ITS). There are many cognitive machines that make up higher cognition, however, the one to be pre-eminently studied by this branch of cybernetics is rationality, understood as a mechanism which allows the development of coherence within the thought system and also its relationship to language, understood as the cognitive machine that complements rationality and also the one that allows the bridging of cognitive systems, thus fostering socialization. The high degree of flexibility of human cognition requires that we think of much of the human cognitive architecture not as determining specific thoughts and behaviors but as an abstract set of mechanisms that potentiates a vast range of capabilities.

The following can be stated as reasons for the development of a new version of second-order cybernetics 4.0:


Although there are resonances between Mancilla's [17] notion of fourth-order cybernetics and the one advanced in this study (second-order 4.0 cybernetics), the main difference between them lies in their approaches: the former adopts a psychological, post-modern approach as well as the requirements of the industry 4.0, which assimilate cybernetics into this discipline and school of thought respectively, while the latter attempts not an interdisciplinary approach, but a transdisciplinary one, i.e., it attempts to develop a model that does not fit within the boundaries of a specific branch of social sciences, but one that respects the basic tenets of cybernetics.

### *3.2.3 Cognitive machines*

When we talk about cognitive machines, we talk about *programmed learning* (or *programmed instruction*) which is a research-based system which helps learners work successfully. The method is guided by research done by a variety of applied psychologists and educators. Anticipating programmed learning, Edward L. Thorndike wrote in 1912:

*If, by a miracle of mechanical ingenuity, a book could be so arranged that only to him who had done what was directed on page one would page two become visible, and so on, much that now requires personal instruction could be managed by print.*

On the basis of these premises Skinner developed programming learning, the theory of programmed instruction (programmed sequences), which he proposed as early as 1958 [18]. According to Skinner, the basic and most important goal of programmed instruction *is to carry out learning in a controlled environment.* His scheme of programmed instruction was to present the material as part of a "schedule of reinforcement" in typical behaviorist manner. The programmed text of Skinner's theory of behaviorism is the most complete example of his ideas in action. Skinner's system was generally called "linear programming" because its activities were placed in otherwise continuous text. He laid the foundations of this instruction, which should pursue three mainly objectives:

1.It should provide information in smaller (substantive) sets,

2.it is intended for self-learning and

3.provides immediate background checks and feedback to the learning.

Today's learning systems could be built on the same basic idea, although the range of possibilities for preparing such learning environments is much wider. Whenever we refer to AI in this book, we will be referring to intelligent teaching/learning environments, and usually we will use the term intelligent tutoring system (ITS).

Two questions are to be asked in order to understand todays model-ITS as a universal meta-model or cognitive machine:


As it was already mentioned, there are three requirements to be fulfilled in order for a machine to be considered as cognitive:


The defining features of cognitive machines can be expounded by analyzing their relation to their inputs and outputs. Cognitive machines receive, create, transform and transmit information, which is both their input and output, and which can be used either to create new data, different from that received or to broaden the existing information storage in the brain. This can result in the expansion of the cognitive domain. This means that cognitive machines are omnipoietic because they can produce both their own components and other information; omnipoiesis, the

**Figure 4.**

*Algorithm of a cybernetic learning system.*

#### **Figure 5.** *LMS\_AI.*

ability to create all kinds of output (internal and external to self) is the distinguishing feature of cognitive machines, which are the subject of study of our secondorder 4.0 cybernetic system.

### **3.3 Second-order 4.0 cybernetic learning algorithm**

Let us now transfer these theoretical findings onto a concrete example of modern innovative learning environments. **Figure 4** shows an algorithm for a cybernetic learning system (universal meta-model) on the basis of *second-order 4.0 cybernetic systems* and the *didactics of learning theory 4.0* [12]*.*

### **3.4 Education 4.0 - case study: (ITS) based on intelligent solution: LE\_LMS\_AI**

On the basis of the presented theory and the algorithm in **Figure 4**, we present (see **Figure 5**) an intelligent tutoring system ITS (a learning management system or LMS) based on second-order cybernetic pedagogy 4.0 and AI solutions. We named it the *LE\_LMS\_AI.* The LE\_LMS\_AI is heuristically based on a hybrid cybernetic second-order system 4.0. Since the educational system is a complex, multidimensional, non-linear and dynamic system, our findings will be presented using a simplified concrete example.

### **4.** *General description of the* **LE\_LMS\_AI**

Based on the concept shown in **Figure 5**, we developed the LE\_LMS\_AI, consisting of three permanent system modules (the personalized module, the evaluation module, and the communication module) and one module relating to the subject matter at hand (the subject module), which can be independently adapted and/or altered by the teacher. The basic functions of the individual modules are as follows:

The *personalized module* (PM) is a connecting system between the individual learner and the learning system LMS\_AI. It is a link between the teacher and the learner, as well as a link between the learners themselves during their engagement in participatory classes. The PM is closely linked to the evaluation module (EM). Its primary task is to adapt the learning path to an individual learner (individualization and personalization), to determine their initial state (the level of knowledge about a particular topic (learning content) and the learner's attitude regarding this topic), to monitor their progress and adapt the learning path to their needs (e.g., their learning style) and abilities (differentiation). Since both system modules are AI-based, this module is used to store the personal data of an individual, for whom the learning system has been adjusted already at the beginning by modifying the subject module (SM), in order to fit his/her needs and abilities, as defined during previous lessons (i.e. previous SM). If at the beginning of the school year the

### *Artificial Intelligence in Education DOI: http://dx.doi.org/10.5772/intechopen.96498*

LE\_LMC\_AI is the same for all students, at the end of the school year we will have as many different LE\_LMC\_AI as there were students in the class. They will have all achieved the same learning goals and met the same learning standards, however, they will have reached these goals through entirely different paths.

The e*valuation modu*le (EM) is a module based primarily on AI methods. Its basic purpose is to:

1. analyze the existing condition of


2. and forward the results of these analyses to


The basic scheme of this module is shown in **Figure 6**.

The *subject module* (SM) is a module related to a specific subject, i.e., to the concrete teaching/learning content. This module consists of several elements (blocks) and is founded on the idea of brain-based teaching/learning. The module is shown schematically in **Figure 7** (below). The individual learning contents (activities) are divided into learning units with the duration of approximately 45 to 90 minutes, consisting further of blocks in the duration of 10 to 20 minutes. Such modules can be organized for individual subjects as a whole (intelligent i-textbooks), or they can be organized for individual problems or projects, which are stored in a database and are accessible to teachers as a teaching aid. The modules are set up on the basis of concrete examples/learning situations to facilitate individual problem- or researchbased work in students engaged in formal (here, the modules serve as a teaching aid to the teacher) or non-formal types of learning (self-learning, reinforcing knowledge, homework assignments, etc.). These modules represent the flexible part of the LE\_LMS\_AI, which can be adapted or complemented to meet the existing needs and/or requirements (e.g. a change in the curriculum). A schematic representation of such a module is shown in **Figure 8**.

The basic structure of individual blocks is shown in **Figure 8**.

It is necessary to emphasize, as is apparent from **Figures 6** and **7**, that two processes take place simultaneously within an individual block, and that we are thus tracking:


Each SM is built hierarchically, which enables a differentiation of the knowledge acquisition path, and is automatically adapted to the individual learner. In theory, three levels are anticipated (high, medium and low), while the possibility of


employing AI methods would eventually result in a "complete" personalization of the learning paths. The SM are divided into three groups, depending on the difficulty level of the learning content, and on the ways and methods of acquiring this content, namely:


### *Artificial Intelligence in Education DOI: http://dx.doi.org/10.5772/intechopen.96498*

**Figure 8.** *The structure of the SM block.*

• for higher cognitive levels, the LE\_LMS\_AI can be used predominantly as a selflearning tool for the student, supported by the teacher's research-, problem-, or project-based working methods. In this kind of situation, the teacher only appears as a tutor, as the one who provides guidelines and encouragement to the students involved in the learning process. In this kind of process, students are active, curious, and motivated.

The *communication module* (CM) is the link between users (students) and the learning system. It is a module that enables data input, and communication, both between teachers and students, as well as between students and the ITS.

### **4.1 Other possibilities of using AI in education**

### *4.1.1 Student evaluation*

An intelligent program can automatize an entire process of evaluation and unburden the teacher, enabling them to focus on qualitative aspects of lessons. Since the efficiency of machine learning increases proportionately with the expansion of the database, the evaluation of students from the quantitative aspect would not be disputable. The possible mistakes could be corrected by the teacher, and the intelligent system could use this to learn. The time that the teacher would gain with the automatization of evaluation could then be spent for interaction with students, preparing for lessons, or career development, nonetheless leaving her enough time to examine the correctness of the grades, and this would represent the mentioned fail-safe. However, we must be cautious of unpredictable deficiencies of such an approach: the automatization of evaluation may, for example, include the traces of bias and therefore lead to unjust or unrepresentative grades.

### *4.1.2 Individualization of learning*

Intelligent devices are essentially devices the user interface of which and interaction with them are highly individualized. The presence of teaching tools of AI in the educational process will reinforce this aspect of communicating with the world. With the help of intelligent accessories, learning will also become individualized, for the intelligent system can respond to students' needs, focus on certain topics, insist on revising a subject, and determine the learning speed. Here, the question of the goal of such learning arises. Does this program enable, through individualization, the students to develop the necessary cognitive, social and emotional competences:

does it, for example, teach them to be active citizens and not passive consumers? Undoubtedly, the individualized and regularly adjustable learning is a significant pedagogical step; however, it includes certain aspects that need to be analyzed and evaluated, so that the inevitable implementation does not lead to unwanted consequences.

### *4.1.3 Improvement of seminars*

Drawbacks of a seminar are not always obvious, and AI may help teachers to uncover them. *Coursera* is an online seminar platform that is already practicing this. When there is an occurrence of a greater number of students submitting incorrect answers in homework, the system warns the teacher and prepares an individualized message for future students, which contains a hint as to the correct answer. Such an approach helps to eliminate a pedagogical gap and ensures that the students get the immediate feedback which helps them to understand a difficult concept.

### *4.1.4 Searching for information*

We seldom pay attention to the AI systems that customize information for us every day. The customization parameters are based on, for example, locations (Google), purchase history (Amazon), or our needs and demands (Siri). Almost all online advertisements are tailored according to our interests and buying preferences. These intelligent systems have a significant role in how we interact with the internet and information in our professional and private lives. And why should matters be any different in the educational process? Here, too, exists the possibility of customizing information that we use for learning. Current generations of graduates have a radically different approach to research compared to their colleagues from a few years ago. The use of new intelligent accessories in education can increase the impact of the customization of information, and that is why it is even more important for us to be capable of correctly assessing their developmental adequacy [19, 20].

### **5. Conclusion**

Machine behavior is one of the most intriguing, nascent fields in AI. Behavioral sciences can support traditional interpretability methods in developing new methods that will help to better understand and explain the behavior of AI. As the interactions between humans and AI become more sophisticated, machine behavior might play a crucial role to enable the next level of hybrid intelligence. From all of the above it can be concluded that at least the following three guidelines should be taken into consideration, especially with respect to using intelligent learning environments in education:


## **Acknowledgements**

"The authors gratefully wish to acknowledge to the Ministry of Education, Science and Sport of the Republic of Slovenia, and European Social Found. This work would not be possible without the support for the project "Innovative learning environments supported with ICT: Innovative Pedagogy 1:1".

The authors wish to express their appreciation to all those who have ensured the quality of this book. Last but not least, thanks to our children and partners, who have inspired us, supported us, and given us the opportunity to be who we are.

## **Author details**

Andrej Flogie and Boris Aberšek\* Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia

\*Address all correspondence to: boris.abersek@um.si

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Artificial Intelligence in Education DOI: http://dx.doi.org/10.5772/intechopen.96498*

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### **Chapter 7**
