Student Response Systems in Higher Education: Moving Beyond Linear Teaching and Surface Learning
Journal of Educational Technology Development and Exchange, Vol: 1, Issue: 1
2008
- 12Citations
- 564Usage
- 87Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations12
- Citation Indexes12
- CrossRef12
- Usage564
- Downloads296
- Abstract Views268
- Captures87
- Readers87
- 87
Article Description
Over the past decade, instructors in colleges and universities increasingly have used Student Response Systems (SRSs)--typically in large classes to increase the level of student engagement and learning. Research shows that both students and instructors perceive SRSs to be beneficial, although evidence of improved learning has been less clear. Experts emphasize that instructors must consider how technology might enhance good pedagogy in order for increases in learning to occur. SRSs do increase student engagement and provide prompt feedback—two key practices that promote learning. However, professional groups propose goals for students in higher education that focus on deep learning rather than the knowledge-centered emphasis of many large classes. Recent research shows that SRSs coupled with pedagogical enhancements can promote deep learning when teaching and questioning strategies center on higher-level thinking skills. A framework integrating the levels of student responses with principles for good pedagogical practice is provided as a guide for using SRSs to foster deep learning
Bibliographic Details
University of Southern Mississippi
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