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Modeling Student Affective State Patterns during Self-Regulated Learning in Physics Playground
This dissertation research focuses on investigating the incidence of student self-regulated learning behavior, and examines patterns in student affective states that accompany such self-regulated behavior. This dissertation leverages prediction models of student affective states in the Physics Playground educational game platform to identify common patterns in student affective states during use of self-regulated learning behavior. In Study 1, prediction models of student affective states are developed in the context of the educational game environment Physics Playground, using affective state observations and computer log data that had already been collected as part of a larger project. The performances of student affective state prediction models generated using a combination of the computer log and observational data are then compared against those of similar prediction models generated using video data collected at the same time. In Study 2, I apply these affective state prediction models to generate predictions of student affective states on a broader set of data collected from students participants playing Physics Playground. In parallel, I define aggregated behavioral features that represent the self-observation and strategic planning components of self-regulated learning. Affective state predictions are then mapped to playground level attempts that contain these self-regulated learning behavioral features, and sequential pattern mining is applied to the affective state predictions to identify the most common patterns in student emotions.
Findings from Study 1 demonstrate that both video data and interaction log data can be used to predict student affective states with significant accuracy. Since the video data is a direct measure of student emotions, it shows better performance across most affective states. However, the interaction log data can be collected natively by Physics Playground and is able to be generalized more easily to other learning environments. Findings from Study 2 suggest that self-regulatory behavior is closely associated with sustained periods of engaged concentration and .self-regulated learning behaviors are associated with transitions from negative affective states (confusion, frustration, and boredom) to the positive engaged concentration state.
The results of this dissertation project demonstrate the power of measuring student affective states in real time and examining the temporal relationship to self-regulated learning behavior within an unstructured educational game platform. These results thus provide a building block for future research on the real-time assessment of student emotions and its relationship with self-regulated learning behaviors, particularly within online student-centered and self-directed learning contexts
Stealth Assessment
An approach to performance-based assessments that embeds assessments in digital games in order to measure how students are progressing toward targeted goals.To succeed in today's interconnected and complex world, workers need to be able to think systemically, creatively, and critically. Equipping K-16 students with these twenty-first-century competencies requires new thinking not only about what should be taught in school but also about how to develop valid assessments to measure and support these competencies. In Stealth Assessment, Valerie Shute and Matthew Ventura investigate an approach that embeds performance-based assessments in digital games. They argue that using well-designed games as vehicles to assess and support learning will help combat students' growing disengagement from school, provide dynamic and ongoing measures of learning processes and outcomes, and offer students opportunities to apply such complex competencies as creativity, problem solving, persistence, and collaboration. Embedding assessments within games provides a way to monitor players' progress toward targeted competencies and to use that information to support learning.Shute and Ventura discuss problems with such traditional assessment methods as multiple-choice questions, review evidence relating to digital games and learning, and illustrate the stealth-assessment approach with a set of assessments they are developing and embedding in the digital game Newton's Playground. These stealth assessments are intended to measure levels of creativity, persistence, and conceptual understanding of Newtonian physics during game play. Finally, they consider future research directions related to stealth assessment in education
Social Affordances of Mixed Reality Learning Environments: A case from the Science through Technology Enhanced Play project (STEP)
We describe the design of the Science through Technology Enhanced Play (STEP) project. In STEP, we explore the potential for dramatic play—a form of activity that is particularly familiar to early elementary students—to promote meaningful inquiry about scientific concepts. We report on the first round of design experiments conducted with 120 first and second grade students who investigated how and why different states of matter have different properties. Pre-post analyses indicate that the majority of students learned the content and demonstrate how the affordances of the socio-technical system promoted the transition from individual observation to collective inquiry, how play as the root activity provided agency within that inquiry, and how the teacher and the social norms of the classroom reinforced these productive social processes
5th Annual Research in the Capitol [Program], March 25, 2010
Program of research presentations given at the Capitol by students from the University of Northern Iowa, Iowa State University, and the University of Iowa.https://scholarworks.uni.edu/programs_rcapitol/1007/thumbnail.jp
9th Conference of EARLI's JURE "Models and learning: theory, design and application" : 30th June - 4th July 2006, Tartu, Estonia : [abstracts]
http://www.ester.ee/record=b2158682*es
Movement and force
How – and why – do things move? How do we describe how they move? This chapter looks at ideas and activities concerning movement and force. It deals with two major issues: firstly, ideas children have about motion and the strategies for teaching about motion in the primary school program. This will include some discussion of the different contexts in which movement and force can be studied. Secondly, it looks at the wider context of studying movement and force, linking it with technology and science as a human endeavour
Current Trends in Game-Based Learning
A myriad of technological options can be used to support digital game-based learning. One popular technology in this context is the mobile device, considering its high penetration rate in our societies, even among young people. These can be combined with other technologies, such as Augmented Reality (AR) or Virtual Reality (VR), to increase students’ motivation and engagement in learning processes.Due to this, there is an emergent need to know and promote good practices in the development and implementation of game-based learning approaches in educational settings. This was the motto for the proposal of the Education Sciences (ISSN: 2227-7102) Special Issue “Current Trends in Game-Based Learning”. This book is a reprint of this Special Issue, collecting a set of five papers that illustrate the contribution of innovative approaches to education, specifically the ones exploring the motivational factors associated with playing games and the technology that may support them
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