Video games invert the expected relationship with failure - rather than avoiding it, players actively pursue games where failure is frequent. Generally, prior work has focused on players encountering hard-coded fail-states as defined by the game (e.g., losing a life). However, scholars have pointed out that game communities often set their own goals (e.g., speedrunners), changing what it means for the player to fail. Further, a more recently explored perspective concerns how developers themselves conceptualize and design fail states. Yet, no known work has explored developers’ perspectives on player experiences of failure within a specific genre. This study contributes to this through semi-structured interviews with 20 puzzle game developers, identifying commonalities in their design philosophies, approaches to designing challenge, conceptualizations of failure, and criteria for puzzle quality. Through inductive thematic analysis, we show that puzzle developers reframe failure away from performance-based definitions. Rather than centering on incorrect attempts, they conceptualize failure primarily as complete player disengagement, viewing it as a failure of the design. Further, puzzle developers regard what some might consider failure as an intentional learning mechanism that encourages experimentation and nudges players toward solutions. Through this paper, we show how developers set player expectations and goals to create a unique relationship with “failure”.

Game designers often utilize tutorials to explicitly introduce players to game mechanics. Another design approach is to begin by throwing players into the “shallow end”, introducing them to mechanics through active engagement in easier levels. To compare these different approaches, we created two modified versions of the first 7 levels of the puzzle game Baba Is You: a tutorialized version, and an increased impasse version. Seventy-five players spent up to 20 minutes playing one of the modified versions or the original game and were asked to fill out a survey on their engagement level, perceived difficulty, and understanding of the core game mechanics. Analyses show that players of the tutorialized version made fewer mistakes and rated their version at a lower difficulty than players of the original and increased impasse versions, while all groups scored highly on content knowledge and engagement. This suggests that direct instruction at the start of a game allows players to come to an understanding equal to a learn-by-doing approach more quickly, with fewer mistakes, and without sacrificing engagement. This study does not, however, show that tutorial players are more prepared to take on more challenging content without explicit guidance, a direction proposed for subsequent work.

Digital games offer promising platforms for assessing student higher-order competencies such as problem-solving. However, processing and analyzing the large volume of interaction log data generated in these platforms to uncover meaningful behavioral patterns remain a complex research challenge. In this study, we employ sequence mining and clustering techniques to examine students’ log data in an interactive puzzle game that requires player to change rules to win the game. Our goal is to identify behavioral characteristics associated with the problem-solving practices adopted by individual students. The findings indicate that the most effective problem solvers made fewer rule changes and took longer time to make those changes across both an introductory and a more advanced level of the game. Conversely, rapid rule change actions were linked to ineffective problem-solving. This research underscores the potential of sequence mining and cluster analysis as generalizable methods for understanding student higher-order competencies through log data in digital gaming and learning environments. It also suggests future directions on how to provide just-in-time, in-game feedback to enhance student problem-solving competences.

Computational thinking has become the calling card for re-introducing coding into schools. While much attention has focused on how students engage in designing systems, applications, and other computational artifacts as a measure of success for computational thinking, far fewer efforts have focused on what goes into remediating problems in designing systems and interactions because learners invariably make mistakes that need fixing-or debugging. In this panel, we examine the often overlooked practice of debugging that presents significant learning challenges (and opportunities) to students in completing assignments and instructional challenges to teachers in helping students to succeed in their classrooms. The panel participants will review what we know and don't know about debugging, discuss ways to conceptualize and study debugging, and present instructional approaches for helping teachers and students to engage productively in debugging situations.

Educational researchers have increasingly drawn attention to how students develop computational thinking (CT) skills, including in science, math, and literacy contexts. A key component of CT is the process of abstraction, a particularly challenging concept for novice programmers, but one vital to problem solving. We propose a framework based on situated cognition that can be used to document how instructors and students communicate about abstractions during the problem solving process. We develop this framework in a multimodal interaction analysis of a 32-minute long excerpt of a middle school student working in the PixelBots JavaScript programming environment at a two-week summer programming workshop taught by undergraduate CS majors. Through a microgenetic analysis of the process of teaching and learning about abstraction in this excerpt, we document the extemporaneous prioritization of subgoals and the back-and-forth coordination of problem solving phases. In our case study, we identify that (a) problem solving phases are nested with several instances of context-switching within a single phase; (b) the introduction of new ideas and information creates bridges or opportunities to move between different problem solving phases; (c) planning to solve a problem is a non-linear process; and (d) pedagogical moves such as modeling and prompting highlight situated resources and advance problem solving. Future research should address how to help students structure subgoals and reflect on connections between problem solving phases, and how to help instructors reflect on their routes to supporting students in the problem solving process.

Beginning from the assumption that young children (ages 6-8) are capable of reasoning about complex phenomena [12], we set out to better understand dimensions of the Science through Technology Enhanced Play environment that provided support for children to learn about relationships between multiple levels of an emergent phenomenon [23] states of matter. We conducted interactional analysis [15] of several moments in two classrooms as students developed and refined understanding of rules that connect micro behavior of particles of water to macro understanding about states of matter. We argue that central to students' disciplinary work were (1) multiple forms of agency negotiated within the STEP environment that were deeply intertwined with (2) students' embodiment. Agency and embodiment both supported students' consensus understanding of relationships between levels of the states of matter phenomenon (3) through students' joyful and playful collaborative work. We examine several episodes in detail to explore these findings.