Proposing a Feedback System to Enhance Learning Based on Key Performance Indicators

Proposing a Feedback System to Enhance Learning Based on Key Performance Indicators

Anders Berglund (KTH Royal Institute of Technology, Stockholm, Sweden), Johannes Blackne (KTH Royal Institute of Technology, Stockholm, Sweden) and Niklas Jansson (KTH Royal Institute of Technology, Stockholm, Sweden)
DOI: 10.4018/ijqaete.2014010101
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Abstract

This paper proposes a feedback system that is based on the self-evaluation of perceived productivity as a mechanism for detecting deviations in an engineering design student project. By monitoring key performance indicators, project members used feedback loops to recognize alarming patterns and act accordingly. The study is based on descriptive survey data that addressed three factors of influence: perceived productivity, perception of stage completion, and work-activity distribution. The productivity data was analysed by detecting patterns in the form of peaks and lows and by combining the patterns with qualitative data from observations and documented work activities. Measurements were taken every time the project team got together; 33 occasions during the course of the project, resulting in a total of 280 student responses for productivity (P) and completion (C) and 115 student replies for work activity distribution. The findings provide an extraction of peak values and low values that enables tracking of critical incidents. Through an in-depth activity log, each value was enriched with lessons learned about what took place and the consequences for the project, thus enhancing learning from past activities through systematic feedback sessions. The accumulated set of data provided distinguishable patterns for the project team to interpret. Over time this made student actions more proactive, activity execution more distinct and purposeful, and resource allocation in combination with feedback reflections more refined.
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Background

Learning through feedback systems is a vital ingredient for projects of any length. However, insufficient planning and unrealistic project plans are two major causes for time and cost overruns affecting productivity (Bashir & Thomson, 1999). Past research indicates that there are no consistent methods of measuring productivity (Ramírez & Nembhard, 2004). In engineering design projects, productivity has focused on student-to-lecturer deliveries, e.g. student postings (Kreijns & Kirschner, 2004); the instructor’s interpretation of students’ online design behaviour (Rose, et al., 2007). Yet any stronger link to the student perspective seems less well researched. Consequently, one over-arching goal of this research is to shift any productivity issues in design courses away from an outcome-derived concern to a mechanism that supports learning. In more detail, the study highlights measuring and detecting of patterns in the self-evaluation of students’ perceived productivity. Productivity for this purpose refers to the output of quality work, given a certain input; it is not a ratio between a quantity and a time unit. This study combines self-evaluation and productivity in the sense that the team members themselves evaluated how productive they felt the entire team was during the workday. Carbarry, Lee and Ohland (2010) have been able to provide a link between self-efficacy and productivity in engineering design projects.

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