Integrating collaborative problem solving throughout the curriculum

Daigle, Roy J.; Doran, Michael; Pardue, J. Harold

doi:10.1145/236462.236547

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…In [5] a group problem-solving model, based on MBTI, was introduced to address student deficiencies in problem solving skills and teamwork. MBTI was used to predict academic success and subsequent career satisfaction for engineering students in [6].…”

Section: Introductionmentioning

confidence: 99%

Graduate Engineering Student Performance Assessment: How Learning Pattern Affects Test Score

Chang¹,

Chang²

2000 Annual Conference Proceedings

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In this paper, the findings based on a graduate electrical engineering course titled "Real-Time Control Systems Design" are analyzed and reported. This course comprises of a lecture and laboratory component where the students are expected to transform their theoretical knowledge into a viable team laboratory design and present the results to the entire class. Upon running this course for seven consecutive years, the following observations are, invariably, made: 1. A bimodal distribution exists between theory and experiment: Students achieving the best classroom scores do not always perform well in a free structure designbased experimental project. Similarly, students who rank in the lower quartile for classroom performance may excel in the experimental project. 2. Composition of team members significantly affects the quality of the experiment. Standard indicators such as GPA do not strongly correlate with the experimental outcome. 3. Strong classroom participation does not correlate with overall course success. Administering the Myers-Briggs Type Indicator during the last two years provided an extra analytical dimension. From the MBTI test data and course grades, it is concluded that the "learning patterns" play a significant role in student performance and specifically, in relations to issues 1-3 above. Based on the combined results, a number of recommendations are brought forth for improving curriculum design and student assessment at the graduate level.

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Section: Introductionmentioning

confidence: 99%

Graduate Engineering Student Performance Assessment: How Learning Pattern Affects Test Score

Chang¹,

Chang²

2000 Annual Conference Proceedings

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“…8,9 In both business 21,36 and academic settings, the Meyers-Briggs Type Indicator (MBTI) personality inventory has been used. 13,14,15,16,17,22,24,25,26,29,32,34 The MTBI is based on Jung's theory and 16 personality/ psychological types can be determined based on an individual's stated preferences for perception and judgment and attitudes toward the external world. Wilde 38,39 has developed team role maps for the perception and judgment domains based on Jungian typology and an associated brief, written instrument to assess where a person falls on these dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…The theory is that different steps in the process of problem solving are best linked to different personality types. Daigle 17 summarizes the process in this manner: "facts gathered by Sensing are then analyzed by iNtuition for possibilities. During decision, facts organized by Thinking are then evaluated by Feeling in accordance with the problem requirements."…”

Section: Introductionmentioning

confidence: 99%

Cognitive Diversity And The Performance Of Freshman Engineering Teams

Bielefeldt¹

2009 Annual Conference &Amp; Exposition Proceedings

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Some researchers have indicated that teams with greater cognitive diversity produce superior results. Cognitive diversity can take a variety of forms, but in this work diversity of personality types is explored. The impact of cognitive styles on team performance was evaluated in a freshman environmental engineering (EVEN) course. The students worked on projects involving comparative analysis and some calculations, but no design or intrinsically "creative" requirements. Specifically, student teams in 2006, 2007, and 2008 evaluated solid waste landfills. In 2006 and 2007 the project encompassed three or four periods of in-class direction and work time. In 2008, the project was modified to compare the energy and environmental impacts of landfills to waste-to-energy incinerators and included only two class periods with instructor direction (lecture and question/answer). Three different methods were used to form the student teams. In 2006, student grades in the class to date were used to create groups with similar average scores. In 2007, each team included a non first-year student or a student in the honors program in an attempt to assign a potential "team leader" for each group. An evaluation tool to determine perception and judgment roles based on Jungian typology 38 was administered via a written questionnaire to the students on the first day that the semester-project was introduced. This enabled determination of the cognitive diversity on the pre-assigned teams. In 2008, the cognitive style inventory results were used to create teams with maximum diversity. Earlier in the semester, student groups assigned alphabetically compared drinking water alternatives.

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Trends in teaching informatics

Turner

1998

IFIP Advances in Information and Communication Technology

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Integrating collaborative problem solving throughout the curriculum

Cited by 5 publications

References 10 publications

Graduate Engineering Student Performance Assessment: How Learning Pattern Affects Test Score

Graduate Engineering Student Performance Assessment: How Learning Pattern Affects Test Score

Cognitive Diversity And The Performance Of Freshman Engineering Teams

Trends in teaching informatics

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