Purpose – The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer- based learning environment.

Design/methodology/approach – Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve.

Findings – The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems.

Originality/value – Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.

This study investigated the relationship between uninterrupted time-on-task and academic success of students enrolled in a Massive Open Online Course (MOOC). The variables representing uninterrupted time-on-task, such as number and duration of uninterrupted consecutive learning activities, were mined from the log files capturing how 4286 students tried to learn Newtonian mechanics concepts in a MOOC. These variables were used as predictors in the logistic regression model estimating the likelihood of students getting a course certificate at the end of the semester. The analysis results indicate that the predictive power of the logistic regression model, which was assessed by Area Under the Precision-Recall Curve (AUPRC), depends on the value of off-task activity threshold time, and the likelihood of students getting a course certificate increases as students were doing more uninterrupted learning activities over a longer period of time. The findings from this study suggest that a simple count of learning activities, which has been used as a proxy for time-on-task in previous studies, may not accurately describe student learning in the computer-based learning environment because it does not reflect the quality, such as uninterrupted durations, of those learning activities.

This study investigated whether clustering can identify different groups of students enrolled in a massive open online course (MOOC). This study applied self-organizing map and hierarchical clustering algorithms to the log files of a physics MOOC capturing how students solved weekly homework and quiz problems to identify clusters of students showing similar problem-solving patterns. The usefulness of the identified clusters was verified by examining various characteristics of students such as number of problems students attempted to solve, weekly and daily problem completion percentages, and whether they earned a course certificate. The findings of this study suggest that the clustering technique utilizing self-organizing map and hierarchical clustering algorithms in tandem can be a useful exploratory data analysis tool that can help MOOC instructors identify similar students based on a large number of variables and examine their characteristics from multiple perspectives.

Despite the plethora of weight loss programs available in the US, the prevalence of overweight and obesity (BMI ≥ 25 kg/m2) among US adults continues to rise at least, in part, due to the high probability of weight regain following weight loss. Thus, the development and evaluation of novel interventions designed to improve weight maintenance are clearly needed. Virtual reality environments offer a promising platform for delivering weight maintenance interventions as they provide rapid feedback, learner experimentation, real-time personalized task selection and exploration. Utilizing virtual reality during weight maintenance allows individuals to engage in repeated experiential learning, practice skills, and participate in real-life scenarios without real-life repercussions, which may diminish weight regain. We will conduct an 18-month effectiveness trial (6 months weight loss, 12 months weight maintenance) in 202 overweight/obese adults (BMI 25–44.9 kg/m2). Participants who achieve ≥5% weight loss following a 6 month weight loss intervention delivered by phone conference call will be randomized to weight maintenance interventions delivered by conference call or conducted in a virtual environment (Second Life®). The primary aim of the study is to compare weight change during maintenance between the phone conference call and virtual groups. Secondarily, potential mediators of weight change including energy and macronutrient intake, physical activity, consumption of fruits and vegetables, self-efficacy for both physical activity and diet, and attendance and completion of experiential learning assignments will also be assessed.