Understanding Wheel Spinning in the Context of Affective Factors

Introduction

Mastery learning is a philosophy of teaching and learning that “asserts that under appropriate instructional conditions, virtually all students can learn well, that is, can ‘master,’ most of what they are taught.” The theory further asserts that teachers can deliver instruction so that students learn well, enabling students’ short-term success and preparing students for subsequent learning (Block & Burns, 1976).

Decades of research have investigated mastery learning in the context of computer-aided instruction (see Corbett & Anderson, 1994; Frick, 1990). During the 1970s, researchers began developing new modes of computer-based education, specifically intelligent tutoring systems (ITSs). ITSs were learning environments that make use of artificial intelligence to engage students in deep reasoning based on an understanding of students’ knowledge and behavior (Corbett, Koedinger, & Anderson, 1997). Many of today’s ITSs provide assistance to the student in the form of hints or by breaking a complex problem down into simpler steps for the student to solve individually.

ITSs are effective learning environments for computer-assisted problem solving because they are capable of providing assistance to students who are confused. Prior work estimates that ITSs are at least as good as human tutoring at helping students learn (VanLehn, 2011).

Although it is tempting to assume all students benefit from using an ITS, this assumption does not necessarily hold. Upon reflection, the mastery learning model has weaknesses: If a student requires assistance to solve his first two problems, presenting a third with the hope he will learn the skill is a reasonable strategy. However, if the student has been unable to reach mastery in the first twenty practice opportunities of a skill, it is unlikely that the twenty-first opportunity will enable the student to acquire the skill (Beck & Rodrigo, 2014). In many ITSs, the student does not need to attempt a fixed number of problems, but continues to solve problems until he achieves mastery of the associated skills. In other words, the computer keeps presenting problem after problem for as long the student’s skill is less than mastery level.

Beck and Gong (2013) labeled this phenomenon “wheel spinning”. It refers to students who fail to master a skill in a computer tutor in a timely manner. The name comes from a car spinning its wheels in the snow: there is the illusion of forward progress as the car is expending effort and its wheels are spinning rapidly, yet it is not going anywhere. What exactly a “timely manner” is varies from tutor to tutor, but Beck and Gong’s (2013) prior analysis shows that, after 10 attempts to practice a skill, approximately two-thirds of students would have achieved mastery. Subsequent attempts barely increase this percentage, implying that the last third of students need other interventions.

This paper is an extension of a previously-published short conference paper by Beck and Rodrigo (2014). This paper further explores the phenomenon of students being stuck on a particular skill, investigates what other constructs relate to it, and discusses possible approaches for remediation. It specifically attempts to address the following research questions:

• 1.

  How do students spend their time while using an ITS?

• 2.

  Do students wheel spin within the ITS?

• 3.

  What is the relationship between affect and wheel spinning?

• 4.

  What is the relationship between off-task behavior and wheel spinning?

Study Population, System, And Methodology

In this study of wheel spinning, we measured the relationship between wheel spinning and a variety of affective and cognitive variables. We made use of an existing dataset that was previously analyzed for constructs such as student disengagement (see Rodrigo et al., 2010), help-seeking behavior (see Ogan et al., 2014), affect (see Rodrigo et al., 2011), and carelessness (see San Pedro et al., 2011).