Should we consider efficiency and constancy for adaptation in intelligent tutoring systems?

Abstract

Intelligent Tutoring Systems (ITSs) usually make adaptation decisions based on user models that rely on students’ knowledge. However, there are other interesting indicators, which could be used for adaptation that need further exploration. Students’ efficiency (defined as whether they require a lot of time to achieve correctness in their exercises) and constancy (defined as whether they spend a similar time each day they take exercises in the ITS) are two of these indicators. This work aims to analyze 1) how these variables are distributed among students, 2) their evolution over time, and 3) how they are related to other outcomes. Results show that there are different profiles based on the efficiency; e.g., students with low efficiency that need a lot of time to solve exercises correctly, and low reflective students, among others. Furthermore, efficiency and constancy do not vary on average throughout the course. In addition, students are less constant in their daily time spent when their total time spent and average time per exercise is higher, and more efficient students tend to be more constant. Finally, it was found that neither efficiency nor constancy correlate with better grades. The existence of different profiles based on these variables and that they add a different dimension from student knowledge based on answer on exercises suggest that ITSs can make adaptation based on efficiency and constancy.