Using laptops in engineering courses for real‐time data collection and analysis

Ohland, Matthew W.; Stephan, Elizabeth

doi:10.1002/tl.188

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…First, the predominant method of data collection is the case study (Weaver, 2005;Weaver & Nilson, 2005;Hyden, 2005;Birrenkott, Birtrand & Bolt, 2005;Brown, 2005;Granberg & Witte, 2005;Ohland & Stephan, 2005;Pargas & Weaver, 2005;Stephens, 2005;McVay, Snyder & Graetz, 2005). While this approach offers guidance and advice on best practices for using laptops, the reliability and validity of the data have yet to be confirmed for the general population.…”

Section: Methodological Issuesmentioning

confidence: 99%

Assessing laptop use in higher education classrooms: The Laptop Effectiveness Scale (LES)

Lauricella

Kay

2010

AJET

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Considerable research has been conducted examining the use of laptops in higher education, however, a reliable and valid scale to assess in-class use of laptops has yet to be developed. The purpose of the following study was to develop and evaluate the Laptop Effectiveness Scale (LES). The scale consisted of four constructs: academic use and three areas of non-academic use (communication, watching movies, playing games). Tested on 177 higher education students, the data generated using the LES showed acceptable internal reliability, construct validity, content and convergent validity. Feedback from students suggested that both academic and non-academic constructs assessed by the LES could be expanded to incorporate a wider range of laptop related behaviour. OverviewThere is extensive debate about the efficacy of laptop computers in the classroom. Several studies have identified benefits such as keeping students on task and engaged (Hyden, 2005), or following lectures via PowerPoint or multimedia (Debevec, Shih & Kashyap, 2006). However, other research indicates that using laptops in class can be a disadvantage (Fried, 2008). For example, in lecture based classes, students have been reported using laptops for social activities such as surfing the web and sending emails (Barkhuus, 2005;Barak, Lipson & Lerman, 2006). To date, no systematic measure of inclass laptop behaviour has been designed for higher education. The purpose of this paper is to introduce and evaluate a scale that can be used to assess the in-class effectiveness of laptops. Laptop use in higher educationA number of themes have emerged regarding the use of laptops in higher education including general use, communication, student attitudes toward learning, student achievement, and distractions. Each of these will be discussed in turn. General useResearchers have examined how students use laptops for learning. For example, Demb, Erickson and Hawkins-Wilding (2004) found that 16% of overall laptop use involved typing papers and notes. Arend's (2004) work showed that out of class work such as writing papers, using software programs, searching the Internet, and completing group projects constituted the bulk of laptop use. McVay, Snyder and Graetz (2005) added that students reported using their laptops an average of five hours per day, with 36% of that time being spent on academic activities. 152Australasian Journal of Educational Technology, 2010, 26(2) Communication Students report that using a laptop to communicate with faculty via email is generally gratifying (Mitra & Steffensmeier, 2000), and that with email, they communicate with faculty more often and more freely (Arend, 2004). Traditional office hours are being replaced with email communication (Dickson & Segars, 1999; Reynolds, 2003), as students can ask brief questions of faculty without having to meet in person. This asynchronous communication allows for greater flexibility for students and faculty, and also provides faculty with an electronic record of student advising and counselling (Par...

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Section: Methodological Issuesmentioning

confidence: 99%

Assessing laptop use in higher education classrooms: The Laptop Effectiveness Scale (LES)

Lauricella

Kay

2010

AJET

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“…In past decades, electronic sensors and video analysis software have made data collection easier than ever [1,2]. Computer technology allows students and teachers to create displacement vs. time and velocity vs. time graphs for motion in real time [3].…”

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confidence: 99%

Modeling mousetrap car motion with polynomial functions of time

Johnson¹

2022

Phys. Educ.

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Spring powered cars speed up and then slow down. This paper presents three different methods for modelling this motion. These models express the car’s position and velocity as polynomial functions of time. They can be constructed from only one distance and two time measurements. Mousetrap cars are ideal candidates to be modelled in this way because it is obvious when the spring stops acting to accelerate these cars. This paper presents a spreadsheet which generates positive vs. time and velocity vs. time graphs using these polynomial functions. Students can use this spreadsheet to learn more about the motion of their cars. This tool also enables students to view and evaluate several different representations of that motion.

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“…A book chapter is in press that discusses much more extensively the expectations and evaluation of technology in the classroom. 3…”

Section: Ongoing Laboratory Developmentmentioning

confidence: 99%

Adapting Engineering Laboratories To Enhance Learning Using Real Time Sensors

Ohland¹,

Stephan²,

Sill³

2005 Annual Conference Proceedings

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Clemson's NSF-sponsored EXPerimental Engineering in Real-Time (EXPERT) project is designed to assess the efficacy of using real-time sensors connected to laptops in engineering classes. Earlier papers described the painstaking procedure used to develop parallel laboratories-one set of laboratories using sensors and another set of laboratories not using sensors, both with the same educational objectives and approaches. This rigorous approach is needed to isolate the effect of using the sensors.By studying the use of sensors only in laboratories that can be conducted with or without sensors, we constrain the benefits of this new technology. This paper will address the benefits of adapting the laboratory content and pedagogy to make the best use of the technology without limiting the use of sensors to only laboratories that could be conducted without them. This approach results in learning that cannot be compared experimentally to a control group because of the presence of confounding variables. Nonetheless, benefits to learning are discussed, including the reflections of students.

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Using laptops in engineering courses for real‐time data collection and analysis

Abstract: Teaching first-year engineering students in a laptop environment requires carefully choosing and then adapting teaching methods. This chapter describes how laptops were used, how the students responded, and what the group of participating faculty learned in the process.

Cited by 5 publications

References 17 publications

Assessing laptop use in higher education classrooms: The Laptop Effectiveness Scale (LES)

Assessing laptop use in higher education classrooms: The Laptop Effectiveness Scale (LES)

Modeling mousetrap car motion with polynomial functions of time

Adapting Engineering Laboratories To Enhance Learning Using Real Time Sensors

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