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Educational games and digital game-based learning (DGBL) provide pupils interactive, engaging, intelligent, and motivating learning environments. According to research, digital games can support students’ learning and enhance their motivation to learn. Given the central role teachers play in the learning process, their perceptions of DGBL play a significant role in the usage and effectiveness of game-based learning. This paper presents the main findings of an online research on primary school teachers’ attitudes toward DGBL. Furthermore, the research investigates teachers’ opinions about the functionalities provided by the implemented Multiplication Game (MG) and the newly incorporated teacher dashboard. The MG is an assessment and skills improvement tool that integrates an adaptation mechanism that identifies student weaknesses on the multiplication tables and in its latest version also supports a strong social parameter. Students can be informed about their own progress as well as the progress of their peers in an effort to examine if social interaction or competition can increase players’ motivation, which is a subject that raised some concerns in the teaching community. The paper describes the functional options offered by the MG dashboard and documents the outcomes of an online survey conducted with the participation of 182 primary school teachers. The survey indicated the potential usefulness of MG and the benefits it can offer as a learning tool to improve pupil multiplication skills and help teachers identify individual pupil skills and difficulties and adapt their teaching accordingly. The analysis applied has found a correlation between teachers’ perceptions about MG and their view on using digital games in general.

Learner motivation to self-improve is a crucial effectiveness factor in all modes and settings of learning. Game-based learning was long used for attracting and maintaining students’ interest especially in small ages, deploying means such as scoring, timing, scores of peers (i.e., hall of fame), etc. These techniques can provide recognition for high-scoring players, while also developing a sense of safe “distance” in the impersonal electronic environment for low-scoring players. In addition, constructive feedback on mistakes a player makes can contribute to avoiding similar mistakes in the future, thus achieving better performance in the game, while constructing valuable new knowledge when a knowledge gap is detected. This paper investigates an integrated approach to designing, implementing, and using an adaptive game for assessing and gradually improving multiplication skills. Student motivation is fostered by incorporating the Open Learner Model approach, which exposes part of the underlying user model to the students in a graphically simplified manner that is easily perceivable and offers a clear picture of student performance. In addition, the Open Learner Model is expanded with visualizations of social comparison information, where students can access the progress of anonymous peers and summative class scores for improving self-reflection and fostering self-regulated learning. This paper also presents the feedback received by the preliminary testing of the game and discusses the effect of assessing multiplication skills of primary school pupils using the adaptive game-based approach on increasing pupil motivation to self-improve.

Smart learning environments (SLEs), like all adaptive learning systems, are built around the learner model and use it to support a variety of interventions such as mastery learning, scaffolding, adaptive sequencing, and adaptive navigation support. Open learner models (OLMs) “expose” the learner data to users through easily perceivable visual representations aiming to improve student self-reflection and self-regulated learning and also increase user motivation and even foster collaboration. This chapter presents the evolution and current state of OLMs, summarizes related research in the field emphasizing on OLM types, locus of control between the system and the user and visualizations categorized on the basis of quantized/continuous and structured/unstructured representations. OLM cases implementing typical SLEs features are described, along with representative real-life scenarios of incorporating OLMs in SLEs. Moreover, the chapter provides guidelines for designing effective OLMs and discusses current research trends in this active scientific field.