How Widely Can Prediction Models be Generalized? Performance Prediction in Blended Courses

Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students' performance in face to face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions even for repeated offerings of the same course. In this work, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models on student performance based upon persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This work tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention

A Multiple-View Analysis Model of Debugging Processes

This paper proposes a model for analyzing the reading strategies in software debugging. The model provides quantitative and objective visions to a humans debugging activity, and provides the framework for clarifying good- and/or bad-strategies for program reading. We have conducted a case study to observe the debugging processes under a controlled environment. The observation includes: Both novice debugger and expert debugger could correctly locate an area that seems to have a bug, however, only the expert subject could quickly narrow down that area, reading the faulty (or most suspicious) module only will not generally lead to a shorter debugging time, and the most wellperformed subjects read the module that seems to be a key to find a fault. This case study suggested that explicit and quantitative evaluation of the debugging process becomes possible by using the proposed model