How to choose the most appropriate courses to study throughout the learning process remains a question interested in by many students. Students often choose suitable courses according to their interests, needs, and advice from supporting staff, etc. This paper presents the results in developing a course recommendation system that will select…

We present the results of a meta-analytic synthesis of the literature on the association between the use of frequent low-stakes quizzes in real classes and students' academic performance in those classes. Data from 52 independent samples from real classes (N = 7864) suggests a moderate association of d = 0.42 between the use of quizzes and…

Anticipatory activities provide physical educators powerful opportunities to build relationships with their students, activate and assess background knowledge, and pique students' interest in the day's lesson. In this article, we present four literacy tools that can be easily implemented into anticipatory components of physical education lessons.…

Pre-emption describes a system of automated knowledge creation and intervention that steers the present towards a desirable future, by building on knowledge derived from the past. Folding together temporalities makes it impossible to disprove pre-emption. It is increasingly featured within EdTech, introducing new forms of automated governance into…

Analysis of symmetrically predicted endogenous subgroups (ASPES) is an approach to assessing heterogeneity in an ITT effect from a randomized experiment when an intermediate variable (one that is measured after random assignment and before outcomes) is hypothesized to be related to the ITT effect, but is only measured in one group. For example,…

Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The…

Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent…

Human resources training is considered an effective solution in empowering human resources. Organizations try to have effective educational planning for this precious resource by identifying shortcomings through a need assessment. This study provides a model based on organizational data analysis to achieve a unique and appropriate training…

This paper focuses on the relationships among education, self-tracking technologies, and body practices, addressing an ongoing debate about the 'disciplinary versus empowering' role of health tracking technologies in teaching people how to live. Using a Feminist Science and Technology Studies approach (FSTS), it inquires into the understandings…

In recent times, Educational Data Mining and Learning Analytics have been abundantly used to model decision-making to improve teaching/learning ecosystems. However, the adaptation of student models in different domains/courses needs a balance between the generalization and context specificity to reduce the redundancy in creating domain-specific…

This study approaches the identification and prediction of transformative research topics by using the concepts of catastrophe theory. Based on the evaluation model of catastrophe theory, 11 indicators were selected for four different aspects: growth rate, economic and social influence, network characteristics and the degree of uncertainty in…

Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these…

Predicting student performance in Massive Open Online Courses (MOOCs) is important to aid in retention efforts. Researchers have demonstrated that video watching features can be used to accurately predict student test performance on video quizzes employing neural networks to predict video test grades from viewing behavior including video searching…

No studies have analyzed the presence of mentoring programs on Canadian postsecondary websites. Filling this crucial gap, this study examined the official websites of 96 Canadian postsecondary institutions -- which accounts for every university in Canada -- to assess the presence of mentoring programs. Results suggest that public institutions (n =…

In the context of education big data, it uses data mining and learning analysis technology to accurately predict and effectively intervene in learning. It is helpful to realize individualized teaching and individualized teaching. This research analyzes student life behavior data and learning behavior data. A model of student behavior…