Open AccessJournal Article
Examining the Affects of Student Multitasking with Laptops during the Lecture
TL;DR: This paper presents the results of an exploratory study that investigates different types of student multitasking behavior while using laptop computers in an unstructured manner during class and introduces quantifiable metrics for measuring the frequency, duration, and extent of studentMultitasking behavior in class, and evaluates the impact this behavior has on academic performance.
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Abstract: 1. INTRODUCTION Laptop computers are widely used in many college classrooms today (Weaver and Nilson, 2005); however, there is an ongoing debate regarding the purpose and value of laptop initiative programs that encourage or even require students to purchase laptops, and the role of laptops in classrooms. Although the use of laptops in the classroom has the potential to motivate and contribute to student learning (Efaw, Hampton, Martinez, Smith, 2004; Trimmel and Bachmann, 2004), they also have the potential to negatively impact student attention, motivation, student-teacher interactions, and academic achievement (Young, 2006; Meierdiercks, 2005). Previous research has shown that students who bring laptops to class often engage in electronic multitasking that involves switching their cognitive focus back and forth between tasks that are directly related to the lecture material and tasks that are not directly related to the lecture material (Fried, 2008; Hembrooke and Gay, 2003; Grace-Martin and Gay, 2001). Although many students may believe they can switch back and forth between different tasks with no serious consequences to their academic performance, multitasking has been shown to dramatically increase the number of memory errors and the processing time required to "learn" topics that involve a significant cognitive load (Rubenstein, Meyer, and Evans, 2001). Attempting to "learn" while engaged in multitasking behavior can result in the acquisition of less flexible knowledge that cannot be easily recalled and/or applied in new situations (Foerde, Knowlton, and Poldrack, 2006). Furthermore, it takes time and effort to refocus after switching from one task to another (Bailey and Konstan, 2006). It can be argued, that multitasking is a natural part of the modern classroom and work environments and students need to learn to multitask effectively--especially in today's high tech world. Research that investigates how students use laptops in the classroom and what affects laptop usage has on performance outcomes does exist, but there is a lack of research that focuses on the unstructured or unsanctioned use of computers in the classroom, that explicitly measures learning outcomes, and that incorporates actual use data (1). In general, multitasking has been shown to negatively impact productivity (Foerde, Knowlton, and Poldrack, 2006; Rubenstein, Meyer, and Evans, 2001); however, the affects of different types of computer-based multitasking behaviors in the classroom have not been measured and examined in detail to date. This paper presents the results of an exploratory study that investigates different types of student multitasking behavior while using laptop computers in an unstructured manner during class. A number of novel contributions are made. First, we collect both self reported laptop usage data and actual laptop usage data from spyware installed on student laptops. This allows us to directly measure student laptop use, and then compare student's actual usage to self-reported usage. Second, we categorize different types of software multitasking activities and identify which activities are performed most frequently and for how long. We then examine how different categories of distractive software activity impact class performance. We define distractive multitasking as tasks or activities where cognitive resources are used to process information that is not directly related to the course material. Productive multitasking is defined as tasks or activities that are directly related to completing a primary task associated with the course material. Finally, we introduce quantifiable metrics for measuring the frequency, duration, and extent of student multitasking behavior in class, and evaluate the impact this behavior has on academic performance. Three primary research questions are addressed. (1) How does the frequency of multitasking related to each multitasking category affect learning outcomes? …
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References
•Book
A taxonomy for learning, teaching, and assessing : a revision of Bloom's taxonomy of educational objectives
Lorin W. Anderson,David R. Krathwohl +1 more
- 01 Jan 2001
TL;DR: The Taxonomy of Educational Objectives as mentioned in this paper is a taxonomy of educational objectives that is based on the concepts of knowledge, specificity, and problems of objectives, and is used in our taxonomy.
12.2K
•Book
A taxonomy for learning, teaching, and assessing : a revision of Bloom's
Lorin W. Anderson
- 01 Jan 2014
TL;DR: The Taxonomy of Educational Objectives as discussed by the authors is a taxonomy of educational objectives that is based on the concepts of knowledge, specificity, and problems of objectives, and is used in our taxonomy.
9.7K
Long-term working memory
K A Ericsson,Walter Kintsch +1 more
TL;DR: To account for the large demands on working memory during text comprehension and expert performance, the traditional models of working memory involving temporary storage must be extended to include working memory based on storage in long-term memory.
3.3K
Executive control of cognitive processes in task switching.
TL;DR: This article found that task alternation yielded switching-time costs that increased with rule complexity but decreased with task cuing, supporting a model of executive control that has goal-shifting and rule-activation stages for task switching.
In-class laptop use and its effects on student learning
TL;DR: Examination of the nature of in-class laptop use in a large lecture course and how that use is related to student learning showed that students who used laptops in class spent considerable time multitasking and that the laptop use posed a significant distraction to both users and fellow students.