Michael Wixon

Discovery With Models A Case Study on Carelessness in Computer-Based Science Inquiry

In recent years, an increasing number of analyses in learning analytics and educational data mining (EDM) have adopted a “discovery with models” approach, where an existing model is used as a key component in a new EDM or analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its potential to enhance research on learning and learners, and key lessons learned in how discovery with models can be conducted validly and effectively. We illustrate these issues through discussion of a case study where discovery with models was used to investigate a form of disengaged behavior (i.e., carelessness) in the context of middle school computer-based science inquiry. This behavior was acknowledged as a problem in education as early as the 1920s. With the increasing use of high-stakes testing, the cost of student carelessness can be higher. For instance, within computer-based learning environments, careless errors can result in reduced educational effectiveness, with students continuing to receive material they have already mastered. Despite the importance of this problem, it has received minimal research attention, in part because of difficulties in operationalizing carelessness as a construct. Building from theory on carelessness and a Bayesian framework for knowledge modeling, we use machine-learned detectors to predict carelessness within authentic use of a computer-based learning environment. We then use a discovery with models approach to link these validated carelessness measures to survey data to study the correlations between the prevalence of carelessness and student goal orientation.

Exploring the Impact of a Learning Dashboard on Student Affect

Research highlights that many students experience negative emotions during learning activities, and these can have a detrimental impact on behaviors and outcomes. Here, we investigate the impact of a particular kind of affective intervention, namely a learning dashboard, on two deactivating emotions: boredom and lack of excitement. The data comes from a study we conducted with over 200 middle school students interacting with an intelligent tutor that provided varying levels of support to encourage dashboard use. We analyze the data using a range of techniques to show that the learning dashboard is associated with reduced deactivating emotions, but that its utility also depends on the way its use is promoted and on students’ gender.

Carelessness and goal orientation in a science microworld

In this paper, we study the relationship between goal orientation within a science inquiry learning environment for middle school students and carelessness, i.e., not demonstrating an inquiry skill despite knowing it. Carelessness is measured based on a machine-learned model. We find, surprisingly, that carelessness is higher for students with strong mastery or learning goals, compared to students who lack strong goal orientation.

When the Question is Part of the Answer: Examining the Impact of Emotion Self-reports on Student Emotion

A variety of methodologies have been put forth to assess students’ affective states as they use interactive learning environments (ILEs) and intelligent tutoring systems (ITS), such as classroom observations and subjective coding, self-coding by students after replays, as well as self-reports of student emotion as students are using the learning environment. Still, it is unclear what the disadvantages of each methodology are. In particular, does measuring affect by asking students to self-report alter student affect itself? The following work explores this question of how self-reports themselves can bias affective states, within one particular tutoring system, Wayang Outpost.