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Study success includes the successful completion of a first degree in higher education to the largest extent, and the successful completion of individual learning tasks to the smallest extent. Factors affecting study success range from individual dispositions (e.g., motivation, prior academic performance) to characteristics of the educational environment (e.g., attendance, active learning, social embeddedness). Recent developments in learning analytics, which are a socio-technical data mining and analytic practice in educational contexts, show promise in enhancing study success in higher education, through the collection and analysis of data from learners, learning processes, and learning environments in order to provide meaningful feedback and scaffolds when needed. This research reports a systematic review focusing on empirical evidence, demonstrating how learning analytics have been successful in facilitating study success in continuation and completion of students' university courses. Using standardised steps of conducting a systematic review, an initial set of 6220 articles was identified. The final sample includes 46 key publications. The findings obtained in this systematic review suggest that there are a considerable number of learning analytics approaches which utilise effective techniques in supporting study success and students at risk of dropping out. However, rigorous, large-scale evidence of the effectiveness of learning analytics in supporting study success is still lacking. The tested variables, algorithms, and methods collected in this systematic review can be used as a guide in helping researchers and educators to further improve the design and implementation of learning analytics systems.

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"Serious Games" is a unique industry that is concerned with the training/ learning performance assessment of its clients. It is one of three digital technology industries (along with digital games, and online learning) that are rapidly advancing into the arena of analytics. The analytics from these industries all came from the tracing of user-generated data as they interacted with the systems, but differed from one another in the primary purposes for such analytics. For example, the purpose of game analytics is to support the growth of digital (entertainment) games, while that of learning analytics is to support the online learning industries. Although some game and learning analytics can indeed be used in serious games, they lack specifi c metrics and methods that outline the effectiveness of serious games-an important feature to stakeholders. Serious Games Analytics need to provide ( actionable ) insights that are of values to the stakeholders-specifi c strategies/policies to improve the serious games, and to (re)train or remediate play-learners for performance improvement. Since the performance metrics from one industry are unlikely to transfer well into another industry, those that are optimal for use in the Serious Games industry must be properly identifi ed as Serious Games Analytics -to properly measure, assess, and improve performance with serious games.

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