In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected…

Engaged and disengaged behaviors have been studied across a variety of educational contexts. However, tools to analyze engagement typically require custom-coding and calibration for a system. This limits engagement detection to systems where experts are available to study patterns and build detectors. This work studies a new approach to classify…

Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms…

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term…

Machine learning is a powerful method for predicting the outcomes of interactions with educational software, such as the grade a student is likely to receive. However, a predicted outcome alone provides little insight regarding how a student's experience should be personalized based on that outcome. In this paper, we explore a generalizable…

The challenge of learning programming in a MOOC is twofold: acquiring programming skills and learning online, independently. Automated testing and feedback systems, often offered in programming courses, may scaffold MOOC learners by providing immediate feedback and unlimited re-submissions of code assignments. However, research still lacks…

Improving competence requires practicing, e.g. by solving tasks. The Self-Assessment task type is a new form of scalable online task providing immediate feedback, sample solution and iterative improvement within the newly developed SAFRAN plugin. Effective learning not only requires suitable tasks but also their meaningful usage within the…

Gaming the system, a behavior in which learners exploit a system's properties to make progress while avoiding learning, has frequently been shown to be associated with lower learning. However, when we applied a previously validated gaming detector across conditions in experiments with an algebra tutor, the detected gaming was not associated with…

Analyzing students' activities in their learning process is an issue that has received significant attention in the educational data mining research field. Many approaches have been proposed, including the popular sequential pattern mining. However, the vast majority of the works do not focus on the time of occurrence of the events within the…

The quality of exams drives test-taking behavior of examinees and is a proxy for the quality of teaching. As most university exams have strict time limits, and speededness is an important measure of the cognitive state of examinees, this might be used to assess the connection between exams' quality and examinees' performance. The practice of…

There are a number of novel exercise types that students can utilize while learning Computer Science, each with its own level of complexity and interaction as outlined by the ICAP Framework [10]. Some are "Interactive," like solving coding problems; "Constructive," like explaining code; "Active," like retyping source…

Effective teachers recognize the importance of transitioning students into learning activities for the day and accounting for the natural drift of student attention while creating lesson plans. In this work, we analyze temporal patterns of gaming behaviors during work on an intelligent tutoring system with a broader goal of detecting temporal…

Self-regulated learning (SRL) is a critical 21st -century skill. In this paper, we examine SRL through the lens of the searching, monitoring, assessing, rehearsing, and translating (SMART) schema for learning operations. We use microanalysis to measure SRL behaviors as students interact with a computer-based learning environment, Betty's Brain. We…

In this paper we consider the problem of modelling when students end their session in an online mathematics educational system. Being able to model this accurately will help us optimize the way content is presented and consumed. This is done by modelling the probability of an action being the last in a session, which we denote as the…

Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…