June 24, 2017
June 24, 2017
June 28, 2017
Computing & Information Technology
This evidence based research looks at the impact of a peer learning using the Bauhaus studio model in a first year engineering class with a large emphasis on programming. Each team is formulated with a “more knowledgeable other” (Vygotsky, 1962), based on self-reported prior learning, with the goal of aiding the rest of the team through early programming challenges. In addition to the professor, and teaching assistants, having a peer act as a mentor can yield higher satisfaction and confidence in learners (Salleh et al., 2011). The study looks at surveys and grades from students at a large Midwestern University. The students are broken into teams based on many criteria, one of which is their background in programming. Teams ideally are formed to include at least one ‘ringer’ with prior programming experience. This analysis compares progressive learning outcomes through the term comparing teams with and without ‘ringers’. Data has been collected from 2013, 2014, and 2015 with trends apparent in each of the years across major topics. The major research questions investigate the role of the ringer in the success of the team, as well looking to see if teams that include a low performing student have any common characteristics. This study shows that the formulation of teams around a carefully selected more knowledgeable other can improve the learning of the entire team. In general, the better the ringer score correlates to an increase in the rest of the team’s average. This only extends to a certain degree where if the ringer’s score is too large a gap from the rest of the team, lower performing members can suffer. In general the formation of teams using prior programming experience seems to do no harm and even possibly improve learning outcomes, and the data may suggest further improvements on the use of teams as well.
Lowe, T. A., & Brophy, S. P. (2017, June), Understanding the Impact of Strategic Team Formation in Early Programming Education Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/29055
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