Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

What Video Games Have to Teach Us about Learning and Literacy

This is the first handbook where the world’s foremost “experts on expertise” review our scientific knowledge on expertise and expert performance and how experts may differ from non-experts in terms of their development, training, reasoning, knowledge, social support, and innate talent. Methods are described for the study of experts’ knowledge and their performance of representative tasks from their domain of expertise. The development of expertise is also studied by retrospective interviews and the daily lives of experts are studied with diaries. In 15 major domains of expertise, the leading researchers summarize our knowledge of the structure and acquisition of expert skill and knowledge and discuss future prospects. General issues that cut across most domains are reviewed in chapters on various aspects of expertise, such as general and practical intelligence, differences in brain activity, self-regulated learning, deliberate practice, aging, knowledge management, and creativity.

The Cambridge Handbook of Expertise and Expert Performance

Challenging games help students learn

https://par.nsf.gov/servlets/purl/10054119

Demystifying computational thinking

“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 specific 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—specific 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 identified as Serious Games Analytics—to properly measure, assess, and improve performance with serious games.

Serious Games Analytics: Theoretical Framework

Serious games analytics : methodologies for performance measurement, assessment, and improvement

Audial engagement

Assessment is an issue that is important to educators because without it, there is no way of telling if learners have arrived at the intended destination. Conducting assessments with game-based learning requires new tools and research methodologies because traditional face-to-face techniques do not transfer well into the multi-user virtual environments found in game worlds. Formative assessment is more useful to instructors and learners because it provides multi-point feedback to help them self-reflect and improve on what they are doing. This chapter describes an “all-rounded” assessment system for game-based learning, which take into consideration the needs of multiple consumers (of information), namely, the Administrators, the Trainers or Educators, and the Learners. The assessment system made use of Information Trails ©—a viable assessment methodology to collect user-generated action data as the game-based learning is occurring (hence, an in-process assessment). The data collected are then remotely transmitted to an external data storage using telemetry, and displayed in real-time via a data visualization application known as Performance Tracing Report Assistant © (or PeTRA). The final online assessment report can be tailored differently according to individual needs of the Learners, Trainers, and Administrators.

Information Trails: In-Process Assessment of Game-Based Learning

The behavioral differences between expert and novice performance is a well-studied area in training literature. Advances in technology have made it possible to trace players' actions and behaviors within an online gaming environment as user-generated data for performance assessment. In this study, we introduce the use of string similarity to differentiate likely-experts from a group of unknown performers (mixture of novices and experts) based on how similar their in-game actions are to that of experts. Our findings indicate that string similarity is viable as an empirical assessment method to differentiate likely-experts from novices and potentially useful as the first performance metric for Serious Games Analytics (SEGA).

Christian Sebastian Loh

Papers

Serious Games Analytics: Theoretical Framework

Serious games analytics : methodologies for performance measurement, assessment, and improvement

Audial engagement

Information Trails: In-Process Assessment of Game-Based Learning

Measuring the (dis-)similarity between expert and novice behaviors as serious games analytics