Naturalistic music typically contains repetitive musical patterns that are present throughout the song. These patterns form a signature, enabling effortless song recognition. We investigate whether neural responses corresponding to these repetitive patterns also serve as a signature, enabling recognition of later song segments on learning initial segments. We examine EEG encoding of naturalistic musical patterns employing the NMED-T and MUSIN-G datasets. Experiments reveal that (a) training machine learning classifiers on the initial 20s song segment enables accurate prediction of the song from the remaining segments; (b) β and γ band power spectra achieve optimal song classification, and (c) listener-specific EEG responses are observed for the same stimulus, characterizing individual differences in music perception.

Learning engineering adds tools and processes to learning platforms to support improvement research. One kind of tool is A/B testing, which is common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE). A number of A/B testing systems focused on educational applications have arisen recently, including UpGrade and E-TRIALS. A/B testing can be part of the puzzle of how to improve educational platforms, and yet challenging issues in education go beyond the generic paradigm. For example, the importance of teachers and instructors to learning means that students are not only connecting with software as individuals, but also as part of a shared classroom experience. Further, learning in topics like mathematics can be highly dependent on prior learning, and thus A or B may not be better overall, but only in interaction with prior knowledge. In response, a set of learning platforms is opening their systems to improvement research by instructors and/or third-party researchers, with specific supports necessary for education-specific research designs. This workshop will explore how A/B testing in educational contexts is different, how learning platforms are opening up new possibilities, and how these empirical approaches can be used to drive powerful gains in student learning. It will also discuss forthcoming opportunities for funding to conduct platform-enabled learning research.

Modern neuroscience has shown that the brain is profoundly rhythmic and that frequencies of neural rhythms are responsive to frequencies of musical rhythms. We collected Electroencephalography (EEG) response on 12 naturalistic music stimuli (songs), from 20 participants. We retrieved the tempo and its sub-harmonics from our stimuli (songs), and further used this information to predict the beats in the brain response using Machine Learning techniques. We observed a hierarchy of beats in each of the songs, with a specific beat frequency to be dominant (i.e. higher in magnitude) than others. This led us to form three groups of songs and their brain responses, with each of the groups indicating the frequency of a beat that dominated in the hierarchy of beat structure of that song. We used small segments of 1, 3 and 5 seconds of brain responses, rather than the entire song duration. We further created two sets for classification of the three groups of brain responses and utilized two spatial filtering techniques: Mean across electrodes (ME) and first principal component (PC1), and a Dense method using data from all electrodes. This was followed by feature extraction using band power. We developed univariate and multivariate models for classification to demonstrate the significance of each frequency band which represent beat frequencies. The dense method outperformed ME and PC1. Features related to eighth note generated maximum discrimination between classes. We also observed a positive correlation between window length and rate of correct prediction. Accuracy from one second to five seconds window improved significantly in both the sets. We achieved maximum accuracy of 70% and 56% accuracies for binary and ternary classification respectively, which is 20% above chance-level accuracy. Random Forest and kNN performed better than SVM. This work contributes to the growing body of knowledge to understand the underlying neural mechanism of rhythm processing in the brain.

The emerging discipline of Learning Engineering is focused on putting into place tools and processes that use the science of learning as a basis for improving educational outcomes. An important part of Learning Engineering focuses on improving the effectiveness of educational software. In many software domains, A/B testing has become a prominent technique to achieve the software's goals. Many large companies (Amazon, Google, Facebook, etc.) run thousands of AB tests and present at the Annual Conference on Digital Experimentation (CODE), but that venue is too broad to address AB testing issues specific to EdTech platforms. We see a need to address issues with running large-scale A/B tests within the educational context, where the use of A/B testing lags other industries. This workshop will explore ways in which A/B testing in educational contexts differs from other domains and proposals to overcome current challenges so that this approach can become a more useful tool in the learning engineer's toolbox.

The potential space of game designs is astronomically large. This paper shows how game design theories can be translated into a simple, tangible card deck that can assist in the exploration of new game designs within a broader "design space." By translating elements of game design theory into a physical card deck, we enable users to randomly sample a design space in order to synthesize new game design variations for a new play platform ("Lumies"). In a series of iterative design and testing rounds with various user groups, the deck has been optimized to merge relevant game theory elements into a concise card deck with limited categories and clear descriptions. In a small, controlled experiment involving groups of design students, we compare the effects of brainstorming with the card deck or the "Directed Brainstorming" method. We show that the deck does not increase ideation speed but is preferred by participants. We further show that our target audience, children, were able to use the card deck to develop dozens of new game ideas. We conclude that design space cards are a promising way to help adults and children to generate new game ideas by making it easier to explore the game design space.

No abstract available.

Many game designers aim to optimize difficulty to make games that are "not too hard, not too easy." However, recent experiments have shown that even moderate difficulty can reduce player engagement. The present work investigates other design factors that may account for the purported benefits of difficulty, such as choice, novelty and suspense. These factors were manipulated in three design experiments involving over 20,000 play sessions of an online educational game.

"Multi-armed bandits" offer a new paradigm for the AI-assisted design of user interfaces. To help designers understand the potential, we present the results of two experimental comparisons between bandit algorithms and random assignment. Our studies are intended to show designers how bandits algorithms are able to rapidly explore an experimental design space and automatically select the optimal design configuration. Our present focus is on the optimization of a game design space. The results of our experiments show that bandits can make data-driven design more efficient and accessible to interface designers, but that human participation is essential to ensure that AI systems optimize for the right metric. Based on our results, we introduce several design lessons that help keep human design judgment in the loop. We also consider the future of human-technology teamwork in AI-assisted design and scientific inquiry. Finally, as bandits deploy fewer low-performing conditions than typical experiments, we discuss ethical implications for bandits in large-scale experiments in education.

One consequence of rapid advances in computer technology is the obsolescence of hundreds of millions of computers each year. This paper explores strategies for increasing the reuse of outdated computers through an investigation of an 8-bit home computer that is still popular in developing countries. We observed the use of the computers in 16 households in Ahmedabad and Bangalore, India in order to gain insight into the contextual factors that support the continued popularity of the device. While most computers become obsolete in less than a decade, this 30-year-old computer technology remains useful because it provides exciting, multi-user family entertainment. While having minimal processing power and virtually no connectivity, the 8-bit computer supports input and output channels that are especially suited for co-located social game play. In contrast, PCs are primarily designed for individual use. Therefore, we offer low-cost design recommendations that would enable outdated PCs to support greater shared use and increased utility within the constrained material context of low-income households. These simple interventions, if adopted by computer refurbishment industries, have the potential to significantly extend the useful lifespan of PCs.

Online games can serve as research instruments to explore the effects of game design elements on motivation and learning. In our research, we manipulated the design of an online math game to investigate the effect of challenge on player motivation and learning. To test the \'1cInverted-U Hypothesis\'1d, which predicts that maximum game engagement will occur with moderate challenge, we produced two large-scale (10K and 70K subjects), multi-factor (2x3 and 2x9x8x4x25) online experiments. We found that, in almost all cases, subjects were more engaged and played longer when the game was easier, which seems to contradict the generality of the Inverted-U Hypothesis. Troublingly, we also found that the most engaging design conditions produced the slowest rates of learning. Based on our findings, we describe several design implications that may increase challenge-seeking in games, such as providing feedforward about the anticipated degree of challenge.

Energy feedback systems, particularly residential energy feedback systems (REFS), have emerged as a key area for HCI and interaction design. However, we argue that HCI researchers, designers and others concerned with the design and evaluation of interactive systems should more strongly consider the ineffectiveness of such systems, including not only potential limitations of specific types of REFS or REFS in general but also potentially counterproductive or harmful effects of REFS. In this paper we outline research questions and issues for future work based on critical gaps in REFS research identified from (i) a review of REFS literature and (ii) findings from two qualitative studies of commercial home energy monitors.

'Cognitive Artifacts' is a theoretical framework that may allow a common evaluation of the impact of the products of science and art. Describes need for transformations of science that engage emotional, aesthetic, social and spiritual cognitive processes. Artist describes current work investigating 'Social Architectures.'.