Success in coding exercises is deeply related to the strategy employed by the students to solve coding tasks. In this contribution, we analyze the programming patterns of 600 students from an introductory university course in object-oriented programming. The students were provided unit tests for the assessment of their codes, and their editing and testing actions were recorded using an Eclipse plug-in. The primary motivation for this study is to discover the programming strategies used by the students for coding exercises with different difficulty levels, and find out if any relation exists between these strategies and the success in solving the coding tasks. More insights into this process will enable educators to provide future students timely, appropriate and constructive feedback on their coding process. Thus, to predict the success in the coding exercises, we used indicators from students' testing behaviour reflecting the time and the effort differences between two successive unit test runs. The results show a clear difference in the strategies employed by the students within different success levels. Moreover, the results also highlight the potential ways of providing actionable feedback to the students in a timely and appropriate manner.

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Multi-Modal Data (MMD) can help educational games researchers understand the synergistic relationship between player's movement and their learning experiences, and consequently uncover insights that may lead to improved design of movement-based game technologies for learning. Predicting player performance fosters opportunities to cultivate heightened educational experiences and outcomes. However, predicting player's performance utilising player-generated MMD during their interactions with educational Motion-Based Touchless Games (MBTG) is challenging. To bridge this gap, we implemented an in-situ study where 26 users, age 11, played 2 maths MBTGs in a single 20-30 minute session. We collected player's MMD (i.e., gaze data from eye-tracking glasses, physiological data from wristbands, and skeleton data from Kinect) produced during game-play. To investigate the potential of MMD for predicting player's academic performance, we used machine learning techniques and MMD derived from player's game-play. This allowed us to identify the MMD features that drive rapid highly accurate predictions of players' academic performance in educational MBTGs. This might allow us to provide realtime proactive feedback to the player to support them through their educational gaming experience. Our analysis compared two data lengths corresponding to half and full duration of the player's question solving time. We showed that all combinations of extracted features associated with gaze, physiological, and skeleton data, predicted student performance more accurately than the majority baseline. Additionally, the most accurate prediction of player's performance derived from the combination of gaze and physiological data for both full and half data lengths. Our findings emphasise the significance of using MMD for realtime performance prediction in educational MBTG and offer implications for practice.

The integration of Multimodal Data (MMD) and embodied learning systems (such as Motion Based Educational Games, MBEG), can help learning researchers to better understand the synergy between students' interactions and their learning experiences. Unfolding the dynamics behind this important synergy can lead to the design of intelligent agents which leverage students' movements and support their learning. However, real-time use of student-generated MMD derived from their interactions with embodied learning systems (MBEG in our case) is challenging and remains under-explored due to its complexity (e.g., handle sensor-data and enable an AI agent to use them). To bridge this gap, we conducted an in-situ study where 40 children, aged 9–12, played MBEG on maths and language development. We automatically, unobtrusively, and continuously monitored students' experiences using eye-tracking glasses, physiological wristbands, and Kinect, during game-play. This allowed us to understand the different cognitive and physiological dimensions of students' progress (right/wrong responses) during the three different stages of the MBEG problem-solving processes, namely the “see-solve-move-respond” (S2MR) cycle. We introduce the novel Carry Forward Effect (CFE); a phenomenon occurring in such games, whereby students propagate, or “carry forward,” the cognitive and physiological effects derived from their MMD, to subsequent phases in the see-solve-move-respond cycle. By identifying moments when the Carry Forward Effect is congruent (or not) to students' learning performance, we uncover opportunities for feedback delivery to encourage or subdue the impact of the CFE. Our results demonstrate the importance of wristband and eye-tracking data as key indicators for prioritizing adaptive feedback to support students in MBEG and emphasize the significance of using MMD to support students' performance in real-time educational settings.

Leveraging movement data to support children's learning is appealing and technically challenging. However, there is limited knowledge about exploiting the complete design potential of bodily interplay in learning games. We conducted an in-the-wild study with 8 children, with special educational needs, playing a language based educational motion-based touchless game. We collected children's interaction data (correctness and reaction time), and data regarding the different design elements (game settings) implemented in 90 game sessions. Our analysis shows that number of items on-screen, selection gestures, and time to select items, impact the effectiveness (correctness) and efficiency (reaction time) of the children. We highlight the value of interaction analytics and quantify the relationship between different game design elements and children's efficiency and effectiveness. Our findings help shape the future of learning research by emphasising the substantial benefits of collecting movement data during children's interaction with learning games. CCS CONCEPTS • Human-centered computing → Gestural input; Empirical studies in HCI; Empirical studies in interaction design; Empirical studies in accessibility; • Applied computing → Interactive learning environments.