**Abstract**

In order for educational software coupled with eye-tracking capability to respond with pedagogical appropriateness to a reader's eye movements, reading metrics must be validly interpreted. These metrics and the types of reading they diagnose, for example, scanning, skimming and reading for meaning, come largely from reading fiction texts in a home language. The use of existing classification systems for disadvantaged South African learners did not yield consistent and credible classification of these learners' reading. This could be attributed to learners barking at print, that is, decoding the text without comprehending what they were reading beyond the word level. Eye movements of barkers and non-barkers were analysed and no statistically significant differences were found. Barking at print was found to be distinct from mindless reading and mind-wandering, as well as other reading types for both first and second languages. Barking is characterised by slow reading with few regressions, average fixation durations typical of second language reading, and variability in eye-movements between lines of text. This work is significant in that it establishes that eye-movement during barking at print is distinct from other categories of reading. However, further research is needed before valid applications can be made from this work.

**Keywords:** reading, eye-tracking, eye movements, barking

### **1. Introduction**

Eye-tracking has the potential to enhance learning, for example through incorporation in intelligent learning technologies which make use of artificial intelligence which acts on data gathered from a user to alter the user experience in a personalised manner to optimise learning [1]. Although incorporation of eye tracking technology into such systems is still in its infancy, such technology has already been shown to, for example: improve attention during engagement with intelligent tutoring systems for learning Biology [2], Geography [3] and Computer Programming [4]; Respond to mind-wandering during reading [5]; Detect engagement in metacognitive processing [6]; Predict affect [7]. Particularly if future uses of such technology are aimed at reading-improvement, the software creators must be able to interpret the correspondence between eye-movement metrics and the type of reading the user is undergoing, validly. A well-developed categorisation system does exist for this purpose for a variety of reading types, such as skimming, scanning and reading for meaning at various grade levels, for English home language readers reading fiction, which will be discussed in detail in a subsequent section. Data we collected from a group of poor South African learners reading a science text in English, which is not their home

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[10] Sivic J, Zisserman A. Video Google: A Text Retrieval Approach to Object Matching in Videos. IEEE; 2003. p. 1470

[11] Tomasz A. Using contour information and segmentation for object registration, modeling and retrieval [PhD thesis]. Dublin City

[12] Shotton J, Johnson M, Cipolla R. Semantic text on forests for image categorization and segmentation. In: Computer Vision and Pattern Recognition, 2008 (CVPR 2008). IEEE;

[13] Leibe B, Leonardis A, Schiele B. Combined object categorization and segmentation with an implicit shape model. In: Workshop on Statistical Learning in Computer Vision, ECCV. Vol. 2.5. 2004. p. 7

[14] Bengio S, Weston J, Grangier D. Label em-bedding trees for large

[15] Zhang P, Peng J, Domeniconi C. Kernel pooled local subspaces for classification. In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 35.3. 2005. pp. 489-502

[16] Fergus R, Perona P, Zisserman A. A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition. IEEE; 2005. pp. 380-387

[17] Theocharides T, Vijaykrishnan N, Irwin MJ. A parallel architecture for hardware face detection. In: ISVLSI. Vol.

6. Citeseer; 2006. p. 452

1951;**22**(1):79-86

[18] Kullback S, Leibler RA. On information and sufficiency. The Annals of Mathematical Statistics.

multi-class tasks. In: Advances in Neural Information Processing Systems. 2010.

University; 2006

2008. pp. 1-8

pp. 163-171

**References**

February 2018]

p. 17

pp. 31-38

1989;**1**(4):541-551

[1] Shotton J et al. Real-time human pose recognition in parts from single depth images. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference. IEEE; 2011. pp. 1297-1304

[2] David P. Fueling innovation beyond security—bio-metrics in payments. Available from: https://www. abiresearch.com/market-research/ product/1031105-fueling-innovationbeyond-security-biometr [Accessed: 01

[3] Barbuceanu F, Antonya C. Eye Tracking Applications. In: Bulletin of the Transilvania University of Brasov. Engineering Sciences. Series I 2. 2009.

[4] Quinlan JR. C4.5: Programs for Machine Learning. Elsevier; 2014

[5] Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001;**23**(11):1222-1239

[6] Ballerini L et al. A query-by-example content-based image retrieval system of non-melanoma skin lesions. In: MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support. Springer; 2009.

[7] LeCun Y et al. Back propagation applied to handwritten zip code recognition. Neural Computation.

[8] Platt J et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin

Classifiers. 1999;**10**(3):61-74

[9] Hao F, Qiu G, He H. Feature

combination beyond basic arithmetics. In: BMVC; Citeseer. 2011. pp. 1-11

language, however, could not credibly be categorised using these existing systems, exposing a gap in the literature addressed in this article. This chapter will proceed by giving background on the problem under investigation as well as related studies. This will be followed by a brief discussion on the methodology and an in-depth discussion of the data analysis. The paper will conclude by summarising the significance and limitations of the study.

### **2. Problem statement**

The poorest 80% of South African learners possess, on average, reading skills which rank among the worst in the world [8]. For example, 60% of South African grade 6 learners are unable to read with comprehension in any language [9]. Pretorius and Spaull [10] identify inability to decode text accurately as the primary problem, with barking at print being an additional problem among many of those relatively stronger learners who are at least able to undergo text-decoding. Barking at print refers to engaging in decoding with little to no comprehension of what the text means on a global level, although the meaning of individual words or even groups of words may be comprehended [11]. Such a reading style is consistent with the engagement in superficial textual strategies that strongly characterises poor South African learners' multiple-choice answering patterns [12], for example choosing options containing terms common to or with superficial similarity to the question or to terms in an associated comprehension passage. Barking at print is not unique to South African learners, with the term having been coined by Samuels [13] in the United States, and reports on barking at print even including presence among relatively high achieving learners in what could be considered good schools in affluent areas (see, e.g. [14]). However, given the high prevalence of barking at print among poor South African learners, whom we have easy access to due to our engagement in various intervention programmes for such learners, we are well situated to investigate this reading phenomenon.

Despite the firm establishment of barking at print in education literature, the nearest correspondence in eye-tracking literature is mindless reading, researched by observing participants reading nonsense-text, i.e. text having no meaning in any language [15], as well as reading during mind-wandering (e.g. [5]). Both mindless reading of nonsense text and reading during mind-wandering differ in a number of ways from non-mind-wandering barking at print written in a language which the reader does understand, at least to some extent. These differences include motivation, perceived purpose and prior exposure and expectations to perform each of these activities. Therefore, the findings of mindless reading and mind-wandering research may not correspond to barking at print, and if this is found to be the case, then obviously the usefulness of the existing literature, at least for mindless reading, to applications such as intelligent learning technologies, is limited and a new and more useful set of metrics associated with decoding without comprehension is needed. Further, the eye-tracking metric guidelines resulting from research related to mindless reading and mind-wandering are restricted to gaze length, so that even should barking at print prove to be similar to mindless reading or mind-wandering, there is a gap in the literature about other eye-movement metrics during such reading.

In this study the eye-movement characteristics of 67 grade 8 and 9 South African learners from financially and educationally impoverished backgrounds were examined during silent reading of science text in English, their second language. Based on their comprehension scores, these participants are divided into three groups: barkers (n = 23), poor readers (n = 25) and moderate readers (n = 19). Statistical

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*Eye Movements during Barking at Print DOI: http://dx.doi.org/10.5772/intechopen.81898*

barking at print?

proficiency?

and 219 wpm respectively [16].

amount of effort required.

**3.2 Eye movements during reading**

**3. Background**

**3.1 Reading**

analyses were then performed on the eye-movement characteristics of the barkers relative to that of the other groups of participants, as well as descriptive analyses of the differences between barking and existing literature about other types of read-

1.How do the reading eye-movements of those participants who were barking at print compare to those of their peers of two levels of reading proficiency?

3.How do the eye-movement characteristics of barking at print compare to those published for skimming, scanning and reading for meaning at various levels of

If one is able to read, it means one can look at a word and process its meaning [16]. Rauding, derived from reading and auding, means the ability of a person to understand most of the thoughts contained within the material they are reading [16]. During rauding the eyes move across the lines of words allowing consecutive words to be perceived without needing to concentrate on where the eyes will move next. There are 5 basic reading processes, referred to as gears 1–5, where rauding or gear 3 is the process used most often. Readers can control the rate of input [17] thus the different gears are characterised by different reading speeds, averaging from 138 wpm to 600+ wpm for college students [16]. The goal of the reader determines the gear they use to process the material [16] which changes the reading behaviour [17]. A person's rauding rate is the fastest speed at which they able to successfully process relatively easily text [16]. The average rauding rate for grade 8 and 9 learners is 205

While fluency does not guarantee comprehension, it is essential to be able to comprehend [18]. The four levels of reading which are still applicable today were introduced in 1946 and are as follows: (1) the independent level, (2) the instructional level, (3) the frustration level and (4) the probable capacity level [19]. The fourth level is based on material which is read to a student but the first three are based on the decoding and comprehending ability of the student when reading a text [19], and are therefore focussed on in this article. The word-reading accuracy and comprehension of the first 3 levels are given in **Table 1**. Readers who are able to read at the independent and instructional levels are likely to be able to self-direct their learning through reading, although those at the instructional level would do so sub-optimally unless provided with explicit help. Readers operating at the frustration level are unlikely to engage in voluntary reading activity, given the large

The basic eye movements relevant to reading and visual search are fixations and saccades. Fixations are periods during which the eye is held relatively still in order to focus on an object [20]. Fixations typically last between 200 and 300 ms but the duration is dependent on the task [20]. For example, when reading in English, the

2.What are the eye movement characteristics of those participants who were

ing. For this purpose the following research questions are applicable:

#### *Eye Movements during Barking at Print DOI: http://dx.doi.org/10.5772/intechopen.81898*

analyses were then performed on the eye-movement characteristics of the barkers relative to that of the other groups of participants, as well as descriptive analyses of the differences between barking and existing literature about other types of reading. For this purpose the following research questions are applicable:

