www.learningsummit.eu 14 of instruction and multimedia learning (Mayer 2011, 2020) and 2) Koehler and Mishra’s (2009) Technological, Pedagogical, and Technology Knowledge (TPACK) framework. To address and find conclusive answers, this research utilized a mixed method research approach sampling, testing, and measuring data from research participants enrolled in selected undergraduate business modules at this project’s research site, namely University College Dublin’s (UCD) College of Business (CoB). Research findings prove that the novel learning approach utilizing AR experiences can be an optimum tool to enrich learning experiences in higher business education. The author proposes an Augmented Learning Experience (ALEX) implementation framework offering Higher Education sector’s stakeholders a unique opportunity to successfully include Augmented Reality Learning Objects (ARLOs) into their curriculum. Hence, successful ALEX implementation allows students to engage with curriculum material more meaningfully, resulting in higher levels of student motivation, engagement with learning content, and knowledge acquisition. Consequently, this thesis makes an original and substantial contribution to knowledge in learning and education by introducing a best practice AR Learning Experience (ALEX) implementation framework and associated toolkit for higher business education. The GeoGebra Intelligent m-Tutors Dimitrios Sklavakis1 1. European School Brussels II One-to-one tutoring has proven to be one of the most effective ways of teaching. The implementation of this tutoring model poses the problem of developing Intelligent Tutoring Systems that provide the same tutoring quality as a human tutor. The most successful paradigm is that of Model-Tracing Tutors, which have shown significant success in mathematics. This paper describes a suite of web-based, intelligent model-tracing tutors for 2-dimensional vector geometry developed using GeoGebra and JavaScript. They cover the following competencies: calculation of vector coordinates graphically and from its initial and terminal points; calculation of vector magnitude from its coordinates; calculation of distance of two points from their coordinates; calculation of a segment’s midpoint from the coordinates of its endpoints; investigation of whether two vectors are parallel/vertical or not; calculation of a line’s equation from two points; from its slope and one point; calculation of the point of intersection of two lines; calculation of a line’s equation parallel/vertical to another line; calculation of the acute angle of two lines; calculation of the distance of a point from a line; calculation of the projection of a point to a line; calculation of the distance of two parallel lines. Each tutor guides the student step-by-step, providing feedback and support in every step, which is the cornerstone feature of model-tracing tutoring that makes it so powerful and effective. The tutors are modular, implementing the above atomic and composite competences in various levels of domain cognitive task analysis (micro-, midi- and macro-levels). Implemented as webpages, they call each other to achieve this modularity and scalability. The uniqueness and novelty in the implementation and combination of all these advanced features - model-tracing, modular, micro-, midi- and macro- tutoring - justifies the use of the m- prefix of the GeoGebra intelligent m-Tutors.
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