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While the 2D Pattern Completer and Alternating Color and Shape strategies obtained relatively low kappa values, all detectors achieved moderately high to excellent AUC values, in the range of to Specifically, the Implementing a Full Solution and Abstraction detectors both achieved an AUC value of 0.976, meaning that the detectors can correctly distinguish between the absence and presence of these CT skills 97.6% of the time. Results and Discussion Table 1 presents the best performing algorithm for each CT label in Mudball Wall. These detectors were evaluated using two goodness metrics: Cohen s Kappa and AUC ROC. Detectors were built using 4-fold student-level batch cross-validation, a process in which models are repeatedly built on 75% of the students and tested on the remaining 25%, to estimate model generalizability to new data. The goal of building these detectors is to replace the hand labeling of CT labels with an automated model that can be applied directly to data. Figure 2 shows sample labeled data.ģ Figure 2: Sample annotated Mudball Wall labeling We then built separate detectors for each human-applied label of implicit CT, trying four common classification algorithms used to predict categorical variables from a set of features: W-J48, W- JRIP, step regression, and.

Next, two researchers independently labeled all rounds of Level 1 play from 74 students (37 upper elementary and 37 middle school), achieving acceptable interrater reliability (Cohen s kappa = to across CT skills and strategies). In step 1, researchers watched Zoombinis gameplay independently and refined label definitions of implicit computational thinking, until they agreed that the labeling system was an exhaustive representation of all the CT-related gameplay behaviors seen. Players must use computational thinking skills and strategies to discover the correct patterns of shapes & colors of mudballs to hit dotted cells on the wall and catapult their Zoombinis onward.Ģ Figure 1: Zoombinis screenshot of Mudball Wall Methods Our process of modeling implicit computational thinking includes five steps: 1) Reliable handlabeling of CT behaviors within Zoombinis gameplay, 2.) Synchronizing labels to gameplay process data, 3) Distilling gameplay process data into features useful for measuring behaviors consistent with CT, 4) Building detectors of players CT strategies based on human labeling, 5) Validating the detectors as formative assessments of implicit CT by comparing to performance on external pre/post assessments. We study CT within Mudball Wall, where a large gridded wall blocks the Zoombinis progress (Figure 1). Zoombinis (Authors, 2015) is an award-winning, popular educational game that elicits implicit computational thinking. Success in accurately detecting these skills and strategies represents a new way of providing formative, real-time game-based assessments of CT. In this study, we used learning analytics techniques to build automated detectors able to recognize students implicit Computational Thinking (CT) skills and strategies from gameplay log data. Learning analytics methods offer unique opportunities for providing scalable, replicable measures of implicit learning in games (Authors, 2017). As such, assessing implicit CT through gameplay is not only valuable in its own right, but can also help educators broaden and deepen STEM experiences for diverse learners. Previous studies have shown a potential link between cognitive differences and computational thinking, with students with autism excelling at pattern recognition (Dawson et al., 2007) and learners with ADHD demonstrating superior performance on idea generativity than adolescents without ADHD (Abraham et al., 2006). Computational thinking (CT) is important to the development of 21st century skills (Voogt et al., 2015), and may also support the inclusion of learners with cognitive differences. Well-designed games provide the opportunity to support the development and measurement of implicit learning, which is argued to be foundational for explicit learning in the classroom (Polyani, 1966). Victoria Almeda, Ph.D., EdGE at TERC Elizabeth Rowe, Ph.D., EdGE at TERC Jodi Asbell-Clarke, Ed.D., EdGE at TERC Richard Scruggs, M.Ed., University of Pennsylvania Ryan Baker, Ph.D., University of Pennsylvania Erin Bardar, Ph.D., EdGE at TERC Santiago Gasca, M.A., EdGE at TERC ABSTRACT Introduction There is increasing evidence that games can play a significant role in promoting STEM learning for children and young adults (Authors, 2017). 1 Modeling Implicit Computational Thinking in Zoombinis Mudball Wall Puzzle Gameplay Authors: Ma.

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