We present a method for new ideas selection, which uses text mining and clustering algorithms to filter ideas, while considering the seeker’s goals and the learning dynamics. We focus on the bounded rationality of the solution seeker, who often estimates that the main cost of an idea challenge comes from the reward itself. Yet, making mistakes in the selection process and picking the wrong idea might result in wasting time and money. Our model is an early-stage application of design science research, and its research contribution can be classified as “exaptation”: a solution to a new problem based on known components that can be easily implemented. We present a prototype that its freely available online to be tested by practitioners, and we illustrate how it works, by using real data obtained from two idea challenges.

Imagine a class of students being allowed to do their final exam twice in a row: the first time, participants do their exam individually and with closed books (Exa01); the second time, they solve the same exam in groups and with open books (Exa02). If you think that all students will get a better grade in the second exam, you would be surprised by the results. This article is part of an ongoing project to develop a method for team-based learning named Testudo. We present an assessment technique called DuoTest, which uses a mixed model to (a) analyze data from individual and group exams and (b) determine the positive (or negative) effect of each team over the individual performances. Empirical results collected from 70 students show that individual exams are a weak predictor of the group scores, whereas the fixed effects of each team are a better predictor of Exa02

This article is part of an ongoing project to develop a method for team-based learning named Testudo. We present an assessment technique called DuoTest, which allows students to do their final exams twice in a row: the first time, participants do their exam individually (Exa01); the second time, they solve the same exam in groups (Exa02). By comparing individual and group exams, the system induces the positive (or negative) effect of each team over the individual performances. Empirical results collected from 70 students show that scores of the individual exam (Exa01) are a reliable, although weak, predictor of the group scores (Exa02) (p<0.10, Adj R2= 0.02). By measuring the fixed effect of each team, we obtain a better predictor of Exa02 (Adj R2= 0.71). We conclude our paper by discussing strengths and limitations of our simple approach to assess individual influences over the team and team influence over the individual.

Dynamic visualization starting from Excel file