**5.1. High dimensionality**

Most of the empirical studies, which addressed human HAML, focus on learning two-class problems on a one-dimensional input space [15, 29], but there are obstacles to generalize the model to multiclass classification problems [19]. The same is true for a single-modality (visual) object recognition. It is important to investigate the generalization of human AML to multidimensional objects (stimuli), such as auditory stimuli, high-dimensional stimuli, real-world stimuli, other demographic groups, etc.

**5.6. Prediction of the next question**

**6. Human AML for autism students**

set can improve the child's recognition ability.

In human learning, people often learn by asking rich and interesting questions, which more directly target the concepts in a learning task. For example, a child might ask "Do all dogs have long tails?" or "What is the difference between cats and dogs?" [32]. The main challenge for AML method is to predict which question a human will ask from the given context. A number of recent studies have discussed this challenge [33–35]. Rothe et al. in [33] proposed a model that predicts what questions human learners will ask and can creatively generate novel questions that did not exist in the training data. Their work in [34] showed that human can accurately evaluate question quality by using the Bayesian ideal observers. In the most recent review [35],

Human Active Learning

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http://dx.doi.org/10.5772/intechopen.81371

authors highlight and discuss nine challenges about the psychology of human inquiry.

One goal of recent researches is to incorporate ML models with behavioral models to teach students and to investigate whether they can benefit from ML techniques to learn better.

Radwan et al. [21] were the first to attempt to use AML for teaching students with autism spectrum disorders (ASD). Students with ASD cannot learn in the same way as most people, and they need special treatment to learn a concept or an object. One of the difficulties faced by people with ASD is the recognition of categories. Radwan et al. [21] proposed a novel batchmode pool-based AML framework for teaching students with ASD and compare the effectiveness of PL vs. AML on teaching object recognition for those students. AML approach presented to the student the most informative teaching set of objects based on the uncertainty sampling strategy. In this framework, a student plays the role of the classifier and does not have a probabilistic model. So, the uncertainty is computed in the context of the child's responses to measure informativeness for all objects. If an object's uncertainty is high, it implies that the student does not have sufficient knowledge to classify the object, and then adding this object into the training

For this purpose, a web- and touch-based application was developed and presented on a tablet PC. Objects from everyday lives of children were grouped based on their categories and four difficulty levels L1–L4; see **Figure 5**. Picture stimuli of target objects were colored images, and they were collected using image search engines, in particular Google and Bing. The teaching procedure was based on applied behavioral analysis (ABA) principles. Five students with mild to moderate levels of ASD participated in the experiment. An alternating treatment design of single subject research methods was used to compare the effects of AML and PL.

The results indicate that AML was more effective than PL for four out of the five students. Consequently, students can learn faster and are able to reach a learning criterion with fewer teaching trials [21]. AML approach was generally more effective in terms of accuracy. The statistical results demonstrated that there was a statistically significant difference in accuracy level between the means of PL and AML. The AML approach and procedures provide two
