Web-based support for constructing competence maps: design and formative evaluation

Stoof, A., Martens, R. L., & Van Merriënboer, J. J. G. (2007). Web-based support for constructing competence maps: Design and formative evaluation. Educational Technology, Research and Development, 55(4), 347-368.This article describes the design and formative evaluation of a Web-based tool that supports curriculum developers in constructing competence maps. Competence maps describe final attainment levels of educational programs in terms of - interrelated - competencies. Key requirements for the supportive tool were validity and practicality. Validity refers to internal consistency and meaningful links to the external realities represented. Practicality refers to a design approach of evolutionary prototyping, in which feedback from intended users and domain experts was collected throughout the development process. Formative evaluations of four prototypes were conducted. Measures of design, appeal, goal, content, confidence and relevance showed that the tool is practical. The article describes the formative evaluation process and concludes with a description of the final tool from the perspective of the user and the instructional designer

概念マップと確信度情報に基づく適応的フィードバックに関する研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Tools for the identification and description of competencies

Stoof, A. (2005). Tools for the identification and description of competencies. Unpublished doctoral dissertation. Open University of the Netherlands, The Netherlands

Correctness- and Confidence-based Adaptive Feedback of Kit-Build Concept Map with Confidence Tagging

In this paper, we present an adaptive feedback of Kit-Build concept map with confidence tagging (KB map-CT) for improving the understanding of learners in a reading situation. KB map-CT is a digital tool that supports the concept maps strategy where learners can construct concept maps for representing their understanding as learner maps and can identify their confidence in each proposition of the learner maps as a degree of their understanding. Kit-Build concept map (KB map) has been already realized the propositional level automatic diagnosis of the learner maps. Therefore, KB map-CT can utilize both correctness and confidence information for each proposition to design and distinguish feedback, that is, (1) correct and confident, (2) correct and unconfident, (3) incorrect and confident, and (4) incorrect and unconfident. An experiment was conducted to investigate the effectiveness of the adaptive feedback. The results suggest that learners can revise their maps after receiving feedback appropriately. In “correct and unconfident” case, adaptive feedback is useful to improve the confidence. In the case of “incorrect and confident,” improvement of the propositions was the same ratio with the case of “incorrect and unconfident.” The results of the delay test demonstrate that learners can retain their understanding and confidence one week later.This work was supported by JSPS KAKENHI Grant Number 17H0183901.'Artificial Intelligence in Education' 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part

Fostering the entrepreneur-opportunity nexus in entrepreneurship education - a design science approach

The doctoral thesis uses the Design Science Research approach to investigate key concepts in entrepreneurship education and subsequently develops, tests and evaluates a course design for opportunity recognition in an academic setting at the Karlsruhe Institute of Technology (KIT). Starting with a systematic literature review on entrepreneurial competences published in 2020 (Tittel and Terzidis, 2020), 57 critical entrepreneurial competences were compiled and categorized into an entrepreneurial competence framework. In addition, a conceptual definition of competence and entrepreneurial competence was developed and presented to the scientific community. A qualitative study with 26 experts, including five entrepreneurship lecturers, ten entrepreneurs, seven consultants and four company experts, was conducted to validate the list of competences identified in the recent academic literature. The interviews were analyzed based on the text and content analysis framework proposed by Mayring (2014). As a result, the experts could confirm 39 of the initial entrepreneurial competencies. In addition, 22 new competences could be identified through inductive coding of the interviews. Based on that, critical implications for developing entrepreneurial education could be developed and proposed. Both studies identified business idea generation and opportunity recognition as critical entrepreneurial competencies and highly relevant concepts for entrepreneurship. Therefore, a pedagogical intervention was developed, tested and evaluated in 12 entrepreneurship courses at the KIT. A bibliometric analysis was performed to find scientific evidence and relevant associations between Ikigai and entrepreneurship. Using the Ikigai (生き甲斐) framework, a traditional Japanese concept for "life worth living", the four key pillars (What you love, what you are good at, what the world needs, what you can be paid for) were operationalized and implemented into the pedagogical setting. The opportunity recognition course framework was then quantitatively evaluated with a structural equation model (SEM) proposed by Hair et al. (2021). As a result, the personal values-business idea fit was identified to influence the business idea’s desirability significantly. The subsequent interviews with the student teams reveal that the perceived profitability of the business idea also plays a crucial role in the perceived desirability of the business idea developed in class

Reciprocal Kit-Building of Concept Map to Share Each Other's Understanding as Preparation for Collaboration

Collaborative learning is an active teaching and learning strategy, in which learners who give each other elaborated explanations can learn most. However, it is difficult for learners to explain their own understanding elaborately in collaborative learning. In this study, we propose a collaborative use of a Kit-Build concept map (KB map) called “Reciprocal KB map”. In a Reciprocal KB map for a pair discussion, at first, the two participants make their own concept maps expressing their comprehension. Then, they exchange the components of their maps and request each other to reconstruct their maps by using the components. The differences between the original concept map and the reconstructed map are diagnosed automatically as an advantage of the KB map. Reciprocal KB map is expected to encourage pair discussion to recognize the understanding of each other and to create an effective discussion. In an experiment reported in this paper, Reciprocal KB map was used for supporting a pair discussion and was compared with a pair discussion which was supported by a traditional concept map. Nineteen pairs of university students were requested to use the traditional concept map in their discussion, while 20 pairs of university students used Reciprocal KB map for discussing the same topic. The results of the experiment were analyzed using three metrics: a discussion score, a similarity score, and questionnaires. The discussion score, which investigates the value of talk in discussion, demonstrates that Reciprocal KB map can promote more effective discussion between the partners compared to the traditional concept map. The similarity score, which evaluates the similarity of the concept maps, demonstrates that Reciprocal KB map can encourage the pair of partners to understand each other better compared to the traditional concept map. Last, the questionnaires illustrate that Reciprocal KB map can support the pair of partners to collaborate in the discussion smoothly and that the participants accepted this method for sharing their understanding with each other. These results suggest that Reciprocal KB map is a promising approach for encouraging pairs of partners to understand each other and to promote the effective discussions.This work was partially supported by JSPS KAKENHI Grant Number 17H01839 and 15H02931.'Artificial Intelligence in Education' 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part