The Proposal of the System That Recommends e-Learning Courses Matching the Learning Styles of the Learners

In providing e-learning, it is desirable to build an environment that is suitable to the student’s learning style. In this study, using the questionnaire to measure the student’s preferences for asynchronous learning and the use of ICT in learning that has been develoed by authors, the relationship between the learning preferences of a student that have been measured before and after the course and his or her adaptability to the course is explored. The result of multiple regression analyses, excluding the changes in learning preferences that may occur duirng the course, shows that a student’s learning adaptability can be estimated to some extent based on his/her learning preference measured before the course starts. Based on this result, we propose a system to recommend e-learning courses that are suitable to a student before the student takes the courses

Developing a Method of Recommending E-Learning Courses Based on Students’ Learning Preferences

In designing e-learning, it is desirable that individual learner’s learning style is considered. This study proposes a way to present the information about the expected adaptability of the course, in which a student wishes to enroll, based on the student’s responses to the learning preference questionnaire administered at the beginning of the course. As the result of applying the real data to the model derived, it was confirmed that it would be possible to estimate the course adaptability before taking the course and to provide the information for the student to improve his/her course adaptability based on the student’s responses to the learning preference questionnaire.15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011), September 12-14, 2011, Kaiserslautern, German

Machine intelligence, adaptive business intelligence, and natural intelligence

Copyright © 2008 IEEEOne of the key observations of the author was that machine intelligence might be defined as the capability of a system to adapt its behavior to meet desired goals in a range of environments. Interestingly, the three components of prediction, adaptation, and optimization constitute the core modules of adaptive business intelligence systems. Clearly, the future of the business intelligence industry lies in systems that can make decisions, rather than tools that produce detailed reports.Zbigniew Michalewicz and Matthew Michalewic

ABAC GSB Freshmen’s Perceptions on Expected Performance Dimensions and Learning Preferences: Implications to Curriculum, Instruction, and Institution Development

This research aims to build comprehensive student profiles to identify perceptions and expectations of the students enrolled in the Master programs of the Graduate School of Business (GSB) at Assumption University of Thailand. The main purpose is to establish curriculum and instructional links between what is offered and what students perceived as relevant learning experiences in the program and contribute towards increased student satisfaction in their master degree education.  Self-administered questionnaires were collected from 379 incoming freshmen MBA students from February to August, 2015. The findings of the study revealed that among the performance dimensions expected by the industry from MBA graduates, the dimensions on English proficiency, ethical behavior, and effective use of IT obtained the highest means. Interestingly, timely achievement and responsibility as well as entrepreneurial spirit scored lowest while based on their profiles, the majority of the freshmen are self-employed. Likewise, the overall students’ preferences on the learning processes, modalities, and learning styles indicated no marked differences of preferences of one or two of these modalities and activities. This indicates that choices are generalized and would imply the need for a variety of teaching strategies to respond to the variety of learning processes and modalities that would require appropriate learning activities. To conclude on the interface of the three areas of the study namely: the demographic profiles, the expected performance dimensions, and preferred learning processes to areas of development in graduate education - curriculum, instruction, and institution development, certain initiatives for development were recommended such as: the inclusion of a module or course on the entrepreneurship as a basic foundational course for all students enrolled at GSB to support the third dimension of the Unique Identities of an ABAC graduate which is entrepreneurial spirit and leadership; the adoption and utilization of a brain-based holistic and integrative model of the experiential learning cycle by all lecturers to provide for the use of a variety of teaching modalities and learning activities in all courses. Further it is concluded that Quality Education at any level must come from the interface of quality curriculum, quality instruction, and quality organization. These three areas are intimately interactive and interrelated to achieve the desired outcomes of higher education and realize the vision of AU in “educating intelligences and active minds to change the world.

Online Learning vs. Offline Learning in an MIS Course: Learning Outcomes, Readiness, and Suggestions for the Post-COVID-19 World

This study aims to compare learning outcomes and technology readiness in online versus offline learning and to find suggestions from the viewpoint of learners. Besides, this study also compares differences in students’ perceptions among learning styles. The associations between several factors such as experience and gender, and learning preferences are also explored. A questionnaire was developed to gather data from students who enrolled in an MIS course during the middle of the COVID-19 pandemic. Around half of the students were assigned to study the topic ‘using MS Excel basics’ in offline sessions, whereas the rest were assigned to learn through recorded videos online. Responses from 44 students, together with their comments and suggestions, were used for data analysis. This study found that both online and offline delivery methods can improve students’ cognitive processes according to the Revised Bloom’s Taxonomy and their topic interest significantly. On-campus classes could significantly enhance students’ class attendance intention, but online classes could not. The cognitive process of RBT in terms of evaluating MS Excel content and class attendance intention of online students were significantly lower than offline students. Students also felt that place, equipment, and software on-campus were more ready than online environments. This work provides guidelines for both lecturers and universities in choosing teaching methods for using basic tools after the COVID-19 situation pass, selecting proper course types, designing course activities, and providing sufficient supports for better online learning outcomes. Research gaps suggested by past studies are filled up in this study

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation