As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…

One of the challenges of discussing validity arguments for machine scoring of essays centers on the absence of a commonly held definition and theory of good writing. At best, the algorithms attempt to measure select attributes of writing and calibrate them against human ratings with the goal of accurate prediction of scores for new essays.…

The prediction of dangerous and/or violent behavior is particularly important to the conduct of the U.S. criminal justice system when it makes decisions about restrictions of personal freedom, such as preventive detention, forensic commitment, parole, and in some states such as Texas, when to permit an execution to proceed of an individual found…

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for…

To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…

Over the past half century, computational chemistry has evolved from a niche field to a ubiquitous pillar of modern chemical research. Driven by the increased demand for computational chemistry in research settings, the undergraduate curriculum has evolved alongside to ensure that students are well-equipped for modern research. Toward this end,…

Recollection rejection is traditionally defined as using verbatim traces of old items' presentations to reject new similar test cues, in old/new recognition (e.g., rejecting that "couch" is old by retrieving verbatim traces of "sofa"'s presentation). We broaden this conceptualization to include (a) old as well as new similar…

The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aims to predict learners' performance in online learning settings. This systemic model integrates cognitive, social,…

Game-based learning environments are designed to provide effective and engaging learning experiences for students. Predictive student models use trace data extracted from students' in-game learning behaviors to unobtrusively generate early assessments of student knowledge and skills, equipping game-based learning environments with the capacity to…

A common recommendation in critiques of datafication in education is for greater conversation between the two sides of the (critical) divide -- what might be characterised as sceptical social scientists and (supposedly) more technically-minded and enthusiastic data scientists. This article takes the form of a dialogue between two academics…

Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov…

Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their…

Feinberg and Wainer (2014) provided a simple equation to approximate/predict a subscore's value. The purpose of this note is to point out that their equation is often inaccurate in that it does not always predict a subscore's value correctly. Therefore, the utility of their simple equation is not clear.

Knowledge tracing is a popular and successful approach to modeling student learning. In this paper we investigate whether the addition of neuroimaging observations to a knowledge tracing model enables accurate prediction of memory performance in held-out data. We propose a Hidden Markov Model of memory acquisition related to Bayesian Knowledge…

All students, from college freshmen to advanced graduate students, have asked themselves, "Will this decision make me happy?" The vast majority of them have been wrong. Affective forecasting, the process of predicting future feelings, is a topic of great interest to students due to its applicable and highly relatable nature. This article…