An official website of the United States government
Esports, like traditional sports, face governance challenges such as foul play and match fixing. The esports industry has seen various attempts at governance structure but is yet to form a consensus. In this study, we explore esports governance in League of Legends (LoL), a major esports title. Through a two-stage, mixed-methods analysis of rule enforcement that Riot Games, LoL's developer and publisher, has performed against esports participants such as professional players and teams, we qualitatively describe rule breaking behaviors and penalties in LoL esports, and quantitatively measure how contextual factors such as time, perpetrator identity, and region might influence governance outcomes. These findings about rule enforcement allow us to characterize the esports governance of LoL as top-down and paternalistic, and to reflect upon professional players' work and professionalization in the esports context. We conclude by discussing translatable implications for esports governance practice and research.
more »
« less
"A Time and Phase for Everything" - Towards A Self Regulated Learning Perspective on Computational Support for Esports
Computational support for learning in the domain of esports has seen a great deal of attention in recent years as an effective means of helping players learn and reap the benefits of play. However, previous work has not examined the tools from a learning theory perspective to assess if learning is prompted and supported in the right place and time. As a first step towards addressing this gap, this paper presents the results of two studies: a review of existing computational tools, and an online survey of esports' players' learning needs supplemented with qualitative interviews. Using Zimmerman's Cyclical Phase Model of Self-Regulated Learning as a lens, we identify patterns in the types of support offered by existing tools and players' support interests during different learning phases. We identify 11 opportunities for future research and development to better support self-regulated learning in esports.
more »
« less
- Award ID(s):
- 1917855
- PAR ID:
- 10604197
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 6
- Issue:
- CHI PLAY
- ISSN:
- 2573-0142
- Format(s):
- Medium: X Size: p. 1-27
- Size(s):
- p. 1-27
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Computational modeling tools present unique opportunities and challenges for student learning. Each tool has a representational system that impacts the kinds of explorations students engage in. Inquiry aligned with a tool’s representational system can support more productive engagement toward target learning goals. However, little research has examined how teachers can make visible the ways students’ ideas about a phenomenon can be expressed and explored within a tool’s representational system. In this paper, we elaborate on the construct of ontological alignment—that is, identifying and leveraging points of resonance between students’ existing ideas and the representational system of a tool. Using interaction analysis, we identify alignment practices adopted by a science teacher and her students in a computational agent-based modeling unit. Specifically, we describe three practices: (1) Elevating student ideas relevant to the tool’s representational system; (2) Exploring and testing links between students’ conceptual and computational models; and (3) Drawing on evidence resonant with the tool’s representational system to differentiate between theories. Finally, we discuss the pedagogical value of ontological alignment as a way to leverage students’ ideas in alignment with a tool’s representational system and suggest the presented practices as exemplary ways to support students’ computational modeling for science learning.more » « less
-
Researchers and educators have developed a variety of computer-based technologies intended to facilitate self-regulated learning (SRL), which refers to iterative learning processes wherein individuals set plans and goals, complete tasks, monitor their progress and outcomes, and adapt future efforts. This paper draws upon the SRL literature and related work to articulate two fundamental principles for designing SRL-promoting technologies: the Platform Principle and the Support Principle. The Platform Principle states that SRL-promoting technologies must incorporate clear platforms (i.e., tools and features) for engaging in planning, enacting, monitoring, and adapting. The Support Principle states that SRL-promoting technologies must include clear scaffolds for strategies, metacognition, motivation, and independence. These principles can be applied heuristically to formatively assess how and whether given learning technologies enable and scaffold self-regulation. More broadly, these assessments can empower educational technology creators and users to strategically design, communicate, and study technologies aligned with self-regulation. An exemplar application of the framework is presented using the PERvasive Learning System (PERLS) mobile SRL technology.more » « less
-
Most of today’s educators are in no shortage of digital and online learning technologies available at their fingertips, ranging from Learning Management Systems such as Canvas, Blackboard, or Moodle, online meeting tools, online homework, and tutoring systems, exam proctoring platforms, computer simulations, and even virtual reality/augmented reality technologies. Furthermore, with the rapid development and wide availability of generative artificial intelligence (GenAI) services such as ChatGPT, we are just at the beginning of harnessing their potential to transform higher education. Yet, facing the large number of available options provided by cutting-edge technology, an imminent question on the mind of most educators is the following: how should I choose the technologies and integrate them into my teaching process so that they would best support student learning? We contemplate over these types of important and timely questions and share our reflections on evidence-based approaches to harnessing digital learning tools using a Self-regulated Engaged Learning Framework we have employed in our research in physics education that can be valuable for educators in other disciplines.more » « less
-
IntroductionSelf-regulated learning skills are necessary for academic success. While not all students entering post-secondary education are proficient at many of these critical skills, they can be improved upon when practiced. However, self-regulation tends to be highly internal, making it difficult to measure. One form of measurement comes from using data traces collected from educational software. These allow researchers to make strong empirical inferences about a student's internal state. Automatically captured data traces also make it possible to provide automated interventions that help students practice and master self-regulated learning skills. Methods/resultsUsing an experimental methodology we created a set of promising data traces that are grounded in theory to study self-regulated learning within a typical Computer Science course. Extra attention is given to studying the skill of help-seeking, which is both a key to success in CS and requires unobtrusive observation to properly measure. DiscussionWe also make the case for taking a broader perspective with our data collection efforts. The traces identified in this paper are not from one source, but the full ecosystem of software tools common to CS courses.more » « less
