Enhancing the Credibility of the Decision-Making Journey Through Serious Games Learning Analytics

Author:

Havriliuc Louis Doru1,Bleoju Gianita2,Capatina Alexandru2

Affiliation:

1. Simbound EU, Romania

2. “Dunarea de Jos” University of Galati, Romania

Abstract

This chapter proposes a specific learning system and a combined method to devise the learning analytics component as an envisioned solution to inform the development of other digital learning resources which are built for meeting specific, predefined learning objectives. The authors acknowledge the critical challenges of distinguishing between decision making and decision taking on the outcomes of learning. The ambition is to assess learning performance by means of unstructured interviews with participants using a digital marketing simulation game and how this has helped them attain job success working on real digital marketing projects. The authors expect that the consequences of decisions taken on real or realistic business conditions provided by a simulated learning environment should enrich the learning experience with insights (influences, constructs, and variables), unstructured knowledge representation, and rule-based decisions that learners will utilize and be alert and react to in real markets.

Publisher

IGI Global

Reference16 articles.

1. Full Lifecycle Architecture for Serious Games: Integrating Game Learning Analytics and a Game Authoring Tool

2. Learning From Others’ Failures: The Effectiveness of Failure Stories for Managerial Learning

3. Sustainable Higher Education and Technology-Enhanced Learning (TEL)

4. Daniela, L., Visvizi, A., & Lytras, M. D. (2018). How to Predict the Unpredictable: Technology-enhanced Learning and Learning Innovations in Higher Education. In The Future of Innovation and Technology in Education: Policies and Practices for Teaching and Learning Excellence (pp. 11-26). Emerald Publishing Limited.

5. A set-theoretic approach to organizational configurations

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