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Application of Consumptive Metrics to Measure Internship Alignment
2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021 ; : 970-975, 2021.
Article in English | Scopus | ID: covidwho-1948732
ABSTRACT
Internships aim at transitioning students from the academic environment (academic learning at the university) to a professional work environment (industry practice). Our paper aims to objectively evaluate the alignment of learning with practice based on the internship program conducted in Term 1, 2020 (pre-Covid), for our undergraduate students at the College of Technology Innovation studying in the bachelor's program for Computer Science and Information Systems. In order to measure the alignment, from a theoretical perspective, we adopted the framework of Kirkpatrick, which provides a set of "consumptive metrics"for evaluating the learning resources consumed in education and training, using the constructs 'reaction' (how the learners feel, including their personal reactions to the internship training) and 'learning' (measuring the knowledge, skills, or attitudes acquired as a direct result of the training, including mapping to their courses). Using 36 internship student reports collected over a single semester (in which students spent 8 weeks onsite at various organizations in the United Arab Emirates) as a sample for this study, we measured internship results in terms of the learning resources consumed during the internship experience using consumptive metrics to observe its alignment with practice. The results of the study allow academics to reinforce strong areas and improve areas of concern to better align learning with practice. © 2021 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021 Year: 2021 Document Type: Article