Ting-Wen Chang

PLORS: a personalized learning object recommender system

Learning management systems (LMS) are typically used by large educational institutions and focus on supporting instructors in managing and administrating online courses. However, such LMS typically use a “one size fits all” approach without considering individual learner’s profile. A learner’s profile can, for example, consists of his/her learning styles, goals, prior knowledge, abilities, and interests. Generally, LMSs do not cater individual learners’ needs based on their profile. However, considering learners’ profiles can help in enhancing the learning experiences and performance of learners within the course. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners to increase their learning. In this paper, we introduce the personalized learning object recommender system. The proposed system supports learners by providing them recommendations about which learning objects within the course are more useful for them, considering the learning object they are visiting as well as the learning objects visited by other learners with similar profiles. This kind of personalization can help in improving the overall quality of learning by providing recommendations of learning objects that are useful but were overlooked or intentionally skipped by learners. Such recommendations can increase learners’ performance and satisfaction during the course.

The effects of presentation method and information density on visual search ability and working memory load

This study investigates the effects of successive and simultaneous information presentation methods on learners visual search ability and working memory load for different information densities. Since the processing of information in the brain depends on the capacity of visual short-term memory (VSTM), the limited information processing capacity of learners may affect the visual search ability and working memory load differently for successive and simultaneous presentations. A change-detection experiment was conducted in this research to analyze visual search ability and working memory load. Two 4 x 4 dot arrays with three information densities were designed for the two presentation methods to test twenty-two participants. The results of the study indicated significant differences between the visual search abilities and the working memory loads for the two types of presentations at higher levels of information densities. Furthermore, significant differences were identified between visual search abilities for different information densities, due to the limited capacity of VSTM. The correlations of visual search ability and working memory load showed that the attention of the learners with higher visual search ability and lower working memory loads would perform better than the learners with lower visual search ability and higher working memory load.

Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms

Abstract Identifying students’ learning styles has several benefits such as making students aware of their strengths and weaknesses when it comes to learning and the possibility to personalize their learning environment to their learning styles. While there exist learning style questionnaires for identifying a students learning style, such questionnaires have several disadvantages and therefore, research has been conducted on automatically identifying learning styles from students’ behavior in a learning environment. Current approaches to automatically identify learning styles have an average precision between 66% and 77%, which shows the need for improvements in order to use such automatic approaches reliably in learning environments. In this paper, four computational intelligence algorithms (artificial neural network, genetic algorithm, ant colony system and particle swarm optimization) have been investigated with respect to their potential to improve the precision of automatic learning style identification. Each algorithm was evaluated with data from 75 students. The artificial neural network shows the most promising results with an average precision of 80.7%, followed by particle swarm optimization with an average precision of 79.1%. Improving the precision of automatic learning style identification allows more students to benefit from more accurate information about their learning styles as well as more accurate personalization towards accommodating their learning styles in a learning environment. Furthermore, teachers can have a better understanding of their students and be able to provide more appropriate interventions.

Using Artificial Neural Networks to Identify Learning Styles

Adaptive learning systems may be used to provide personalized content to students based on their learning styles which can improve students’ performance and satisfaction, or reduce the time to learn. Although typically questionnaires exist to identify students’ learning styles, there are several disadvantages when using such questionnaires. In order to overcome these disadvantages, research has been conducted on automatic approaches to identify learning styles. However, this line of research is still in an early stage and the accuracy levels of current approaches leave room for improvement before they can be effectively used in adaptive systems. In this paper, we introduce an approach which uses artificial neural networks to identify students’ learning styles. The approach has been evaluated with data from 75 students and found to outperform current state of the art approaches. By increasing the accuracy level of learning style identification, more accurate advice can be provided to students, either by adaptive systems or by teachers who are informed about students’ learning styles, leading to benefits for students such as higher performance, greater learning satisfaction and less time required to learn.

Toward Recommending Learning Tasks in a Learner-Centered Approach

Learner-centered education becomes more and more popular. One way of offering learner-centered education is to have assignments where learners can select from a pool of learning tasks with different difficulty levels (e.g., many easy tasks, few challenging tasks, etc.). However, a problem that learners can face in such assignments is to select the tasks that are most appropriate for them. In this paper, we introduce a rule-based recommender system that supports learners in selecting learning tasks. Such recommendations aim at helping learners to select the tasks from which they can benefit most in terms of maximizing their learning.

A Rule-Based Recommender System to Suggest Learning Tasks

Learner-centered learning can be defined as an approach to learning in which learners choose the topic to study and learning tasks. Because of available choices, learners can find it difficult to make a decision about which of the topics/tasks would be more appropriate for them. Identifying other learners with similar characteristics and then considering the tasks that worked well, makes it possible to suggest appropriate tasks to a learner. Based on this concept, we introduce a rule-based recommender system that supports learner-centered learning and helps learners to select learning tasks that are most suitable for them, with the focus on maximizing their learning.

An Interactive Course Analyzer for Improving Learning Styles Support Level

Learning management systems (LMSs) contain tons of existing courses but very little attention is paid to how well these courses actually support learners. In online learning, teachers build courses according to their teaching methods that may not fit with students with different learning styles. The harmony between the learning styles that a course supports and the actual learning styles of students can help to magnify the efficiency of the learning process. This paper presents a mechanism for analyzing existing course contents in learning management systems and an interactive tool for allowing teachers to be aware of their course support level for different learning styles of students based on the Felder and Silverman’s learning style model. This tool visualizes the suitability of a course for students’ learning styles and helps teachers to improve the support level of their courses for diverse learning styles.

Adaptive and Personalized Learning Based on Students’ Cognitive Characteristics

Working memory capacity (WMC) is a cognitive characteristic that affects students’ learning behaviors to perform complex cognitive tasks. However, WMC is very limited and can be easily overloaded in learning activities. Considering students’ WMC through personalized learning materials and activities helps in avoiding cognitive overload and therefore positively affects students’ learning. However, in order to consider students’ WMC in the learning process, an approach is needed to identify students’ WMC without any additional efforts from students. To address this problem, we introduce a general approach to automatically identify WMC from students’ behavior in a learning system. Our approach is generic and designed to work with different learning systems. Furthermore, by knowing students’ WMC, a learning system can provide teachers meaningful recommendations to support students with low and high WMC. Accordingly, we created a recommendation mechanism that provides recommendations based on the guidelines of cognitive load theory. These recommendations are intended to assist in presentation of information in order to reduce working memory overload. Information about WMC is also the basis for designing adaptive systems that can automatically provide students with individualized support based on their WMC.

Analyzing Learner Characteristics and Courses Based on Cognitive Abilities, Learning Styles, and Context

Student modeling and context modeling play an important role in adaptive and smart learning systems, enabling such systems to provide courses and recommendations that fit students’ characteristics and consider their current context. In this chapter, three approaches are presented to automatically analyze learners’ characteristics and courses in learning systems based on learners’ cognitive abilities, learning styles, and context. First, a framework and a system are presented to automatically identify students’ working memory capacity (WMC) based on their behavior in a learning management system. Second, a mechanism and an interactive tool are described for analyzing course contents in learning management systems (LMSs) with respect to students’ learning styles. Third, a framework and an application are presented that build a comprehensive context profile through detecting available features of a device and tracking the usage of these features. All three approaches contribute toward building a foundation for providing learners with intelligent, adaptive, and personalized support based on their cognitive abilities, learning styles, and context.

Investigations of Using Interactive Whiteboards with and without an Additional Screen

Interactive whiteboard technologies (IWBs) are becoming pervasive in the school instruction in many parts of the world. The use of single screen based IWB environments are typically used by the teachers to attract interests of students and to interact with them. However, the available display space in such environments is a problematic issue for effective instruction. The research presented in this study looks into the use of IWB with an additional screen in order to extend the display space with the aim to increase the interests of students and interactions between teachers and students. Two investigations were undertaken to analyze the experiences of the teachers in the use of IWB. Based on the findings, this study presents a teaching assistance tool and suggests two teaching scenarios for teachers to support their teaching activities in a dual screen environment using IWBs.