Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years

Fostering Evidence-Based Education with Learning Analytics: Capturing Teaching-Learning Cases from Log Data

Evidence-based education has become more relevant in the current technology-enhanced teaching-learning era. This paper introduces how Educational BIG data has the potential to generate such evidence. As evidence-based education traditionally hooks on the meta-analysis of the literature, so there are existing platforms that support manual input of evidence as structured information. However, such platforms often focus on researchers as end-users and its design is not aligned to the practitioners’ workflow. In our work, we propose a technology-mediated process of capturing teaching-learning cases (TLCs) using a learning analytics framework. Each case is primarily a single data point regarding the result of an intervention and multiple such cases would generate an evidence of intervention effectiveness. To capture TLCs in our current context, our system automatically conducts statistical modelling of learning logs captured from Learning Management Systems (LMS) and an e-book reader. Indicators from those learning logs are evaluated by the Linear Mixed Effects model to compute whether an intervention had a positive learning effect. We present two case studies to illustrate our approach of extracting case effectiveness from two different learning contexts – one at a junior-high math class where email messages were sent as intervention and another in a blended learning context in a higher education physics class where an active learning strategy was implemented. Our novelty lies in the proposed automated approach of data aggregation, analysis, and case storing using a Learning Analytics framework for supporting evidence-based practice more accessible for practitioners

Comparative Study on m-Learning Usage Among LIS Students from Hong Kong, Japan and Taiwan

Mobile learning (m-learning) is gaining its importance in recent years. For libraries, it is inevitable to adapt to this trend and provide various information services and support for m-learning. This paper studies the m-learning usage of Library and Information Science (LIS) students, who will be the new blood for the library in future. In this paper, we invited 267 subjects from Hong Kong, Japan, and Taiwan to participate in our online survey. We found that LIS students from these regions do adopt communication tools and social media for m-learning. However, they less frequently use their smartphones for academic reading. Plus, they rely more on search engines for fulfilling their information needs instead of library resources. We also found that the lack of a mobile version of the library website constitutes a significant barrier in m-learning, but the lack of mobile apps is relatively acceptable by the respondents. The result of this study shows that there are no big differences in m-learning usage among the three regions, except that LIS students from Hong Kong are accessing the learning management platforms via their smartphones more frequently compared to students from Japan and Taiwan.postprin

Graduate Employment Prediction with Bias

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework

Beyond recommendation acceptance: explanation’s learning effects in a math recommender system

Recommender systems can provide personalized advice on learning for individual students. Providing explanations of those recommendations are expected to increase the transparency and persuasiveness of the system, thus improve students’ adoption of the recommendation. Little research has explored the explanations’ practical effects on learning performance except for the acceptance of recommended learning activities. The recommendation explanations can improve the learning performance if the explanations are designed to contribute to relevant learning skills. This study conducted a comparative experiment (N = 276) in high school classrooms, aiming to investigate whether the use of an explainable math recommender system improves students’ learning performance. We found that the presence of the explanations had positive effects on students’ learning improvement and perceptions of the systems, but not the number of solved quizzes during the learning task. These results imply the possibility that the recommendation explanations may affect students’ meta-cognitive skills and their perceptions, which further contribute to students’ learning improvement. When separating the students based on their prior math abilities, we found a significant correlation between the number of viewed recommendations and the final learning improvement for the students with lower math abilities. This indicates that the students with lower math abilities may benefit from reading their learning progress indicated in the explanations. For students with higher math abilities, their learning improvement was more related to the behavior to select and solve recommended quizzes, which indicates a necessity of more sophisticated and interactive recommender system