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The development of games for people with learning disabilities is one way to enhance the quality of learning and respond to the need for inclusive special educational support. Recently, game researchers have highlighted the need for paying more attention to identifying the game design choices that can strengthen learning. This paper reviews recent studies in the field of games that aim at supporting people with difficulties in learning, particularly in basic reading and maths skills. We identify the major characteristics and learning outcomes of the reviewed studies, as well as key design principles that have been used in games for enhancing basic reading and maths skills. The results show that people with specific learning difficulties have positive improvements in the quality of learning. We also found specific gamification elements that have been used to promote the learning of basic reading and maths skills. However, we call for research, which would explicitly examine the effects of game design choices on learning. Currently, the studies that address learning disabilities do not specifically define which kind of games and game design the results refer to, while game design studies do not clarify how these games influence learning. Thus, there is a need to rethink previous empirical studies on game settings for people with learning difficulties via advancing the role of game design in empirical intervention studies.

To better understand the premises for successful computer-supported collaborative learning (CSCL), several studies over the last 10 years have analysed the temporal aspects of CSCL. We broadly define the temporal aspects of CSCL as focusing on the characteristics of or interrelations between events over time. The analysis of these aspects, however, has been loosely defined, creating challenges regarding the comparability and commensurability of studies. To address these challenges, we conducted a systematic literature review to define the temporal analysis procedure for CSCL using 78 journal papers published from 2003 to 2019. After identifying the key operations to be included in the procedure, we studied how the studies implemented these operations. When analysing the temporal aspects of CSCL, six key operations were conducted: (a) proposing theoretically framed research questions (mostly descriptive) regarding the temporal aspects of CSCL; (b) setting up the context (mostly online interaction mediated by communication technologies); (c) collecting process data (mostly asynchronous online discussions); (d) conceptualising events from the process data (mostly communication units, such as messages); (e) conducting one or more temporal analysis methods (mostly social network analysis or sequential analysis); and (f) interpreting the outcomes with the temporal analysis and possible data or method triangulation (mostly sequences of two or more events that had to do with learner interaction or thoughts and ideas developed in the interaction). The temporal analysis procedure can help design both theory-driven studies and methodological experiments advancing CSCL research. Overall, our study increases scholarly understanding regarding the temporal aspects of CSCL.

This study presents new ways of visualising technology-enhanced collaborative inquiry-based learning (CIBL) processes in an undergraduate physics course. The data included screen-capture videos from a technology-enhanced learning environment and audio recordings of discussions between students. We performed a thematic analysis based on the phases of inquiry-based learning (IBL). The thematic analysis was complemented by a content analysis, in which we analysed whether the utilisation of technological tools was on a deep-level, surface-level, or nonexistent basis. Student participation was measured in terms of frequency of contributions as well as in terms of impact. We visualised the sequence of the face-to-face interactions of two groups of five students by focussing on the temporal aspects of IBL, technology enhancement and collaborative learning. First, instead of the amount of time the groups spent on a specific IBL phase, the between-group differences in the most frequent transitions between the IBL phases determined their differential progress in the CIBL process. Second, we found that the transitions were triggered by the groups' ways of utilising technological tools either at the deep level or at the surface level. Finally, we found that the level of participation inequity remained stable throughout the CIBL process. As a result, only some of the members of the groups played a role in the most frequent transitions. Furthermore, this study reveals the need for scaffolds focussing on inquiry, technological and collaborative skills at the beginning of the learning process.

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Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education physics context where computer-supported collaborative inquiry-based learning (CSCIL) is a popular pedagogical approach. Since learners’ needs for support in CSCIL vary in the different inquiry phases (orientation, conceptualization, investigation, conclusion, and discussion), we studied, first, how the coding accuracy of five computational models (based on word embeddings and deep neural networks with attention layers) differed in the various inquiry-based learning (IBL) phases when compared to human coding. Second, we investigated how the different features of the best performing computational model improved the coding accuracy. The study indicated that the accuracy of the best performing computational model (differentiated attention with pre-trained static embeddings) was slightly better than that of the human coder (58.9% vs. 54.3%). We also found that considering the previous and following utterances, as well as the relative position of the utterance, improved the model’s accuracy. Our method illustrates how computational models can be trained for specific purposes (e.g., to code IBL phases) with small data sets by using pre-trained models.

Background: Productive learning processes and good learning outcomes can be attained by applying the basic elements of active learning. The basic elements include fostering discussions and disputations, facing alternative conceptions, and focusing on conceptual understanding. However, in the face of poor course retention and high dropout rates, even learning outcomes can become of secondary importance. To address these challenges, we developed a research-based instructional strategy, the primetime learning model. We devised the model by organizing the basic elements of active learning into a theory-based four-step study process. The model is based on collaborative and technology-enhanced learning, on versatile formative assessment without a final exam, and on genuine teacher presence through intimate meetings between students and teachers. Results: We piloted the model in two university physics courses on thermodynamics and optics and observed persistent student activity, improved retention, and good learning outcomes. Feedback suggested that most students were satisfied with the learning experience. Conclusions: The model suits particularly well for courses that, in addition to the teaching subject itself, focus on teaching balanced study habits and strengthening social integration. By its very construction, it also helps the propagation of research-based instructional strategies. Although the model does contain challenges, it represents a generic framework for learning and teaching that is flexible for further development and applicable to many subjects and levels.

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