Modes and Mechanisms of Game-like Interventions in Intelligent Tutoring Systems

While games can be an innovative and a highly promising approach to education, creating effective educational games is a challenge. It requires effectively integrating educational content with game attributes and aligning cognitive and affective outcomes, which can be in conflict with each other. Intelligent Tutoring Systems (ITS), on the other hand, have proven to be effective learning environments that are conducive to strong learning outcomes. Direct comparisons between tutoring systems and educational games have found digital tutors to be more effective at producing learning gains. However, tutoring systems have had difficulties in maintaining students€™ interest and engagement for long periods of time, which limits their ability to generate learning in the long-term. Given the complementary benefits of games and digital tutors, there has been considerable effort to combine these two fields. This dissertation undertakes and analyzes three different ways of integrating Intelligent Tutoring Systems and digital games. We created three game-like systems with cognition, metacognition and affect as their primary target and mode of intervention. Monkey\u27s Revenge is a game-like math tutor that offers cognitive tutoring in a game-like environment. The Learning Dashboard is a game-like metacognitive support tool for students using Mathspring, an ITS. Mosaic comprises a series of mini-math games that pop-up within Mathspring to enhance students\u27 affect. The methodology consisted of multiple randomized controlled studies ran to evaluate each of these three interventions, attempting to understand their effect on students€™ performance, affect and perception of the intervention and the system that embeds it. Further, we used causal modeling to further explore mechanisms of action, the inter-relationships between student€™s incoming characteristics and predispositions, their mechanisms of interaction with the tutor, and the ultimate learning outcomes and perceptions of the learning experience

Mediated-efficacy: Hope for “helpless” writers

Building on previous studies of college students\u27 writing self-efficacy beliefs, this article presents the empirical foundation for a reconceptualized understanding of this identity process. The study assessed 131 college freshmen enrolled in a developmental writing course who were evaluated holistically using grounded theory methodology. The study identified (a) major theoretical categories revealing the nature of students\u27 initial pessimism about themselves as writers and sense of learned helplessness and (b) a subsequent shift toward optimism and self-efficacy triggered by a particular learning relationship formed with their instructors, the core of the posited mediated-efficacy theory. Implications for college-level developmental writing pedagogy are explored

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

An Architecture for Designing Content Agnostic Game Mechanics for Educational Burst Games

abstract: Currently, educational games are designed with the educational content as the primary factor driving the design of the game. While this may seem to be the optimal approach, this design paradigm causes multiple issues. For one, the games themselves are often not engaging as game design principles were put aside in favor of increasing the educational value of the game. The other issue is that the code base of the game is mostly or completely unusable for any other games as the game mechanics are too strongly connected to the educational content being taught. This means that the mechanics are impossible to reuse in future projects without major revisions, and starting over is often more time and cost efficient. This thesis presents the Content Agnostic Game Engineering (CAGE) model for designing educational games. CAGE is a way to separate the educational content from the game mechanics without compromising the educational value of the game. This is done by designing mechanics that can have multiple educational contents layered on top of them which can be switched out at any time. CAGE allows games to be designed with a game design first approach which allows them to maintain higher engagement levels. In addition, since the mechanics are not tied to the educational content several different educational topics can reuse the same set of mechanics without requiring major revisions to the existing code. Results show that CAGE greatly reduces the amount of code needed to make additional versions of educational games, and speeds up the development process. The CAGE model is also shown to not induce high levels of cognitive load, allowing for more in depth topic work than was attempted in this thesis. However, engagement was low and switching the active content does interrupt the game flow considerably. Altering the difficulty of the game in real time in response to the affective state of the player was not shown to increase engagement. Potential causes of the issues with CAGE games and potential fixes are discussed.Dissertation/ThesisDoctoral Dissertation Engineering 201

Impact of a Coping Model on Novice Learners’ Self-efficacy, Science Learning, and Transfer in a Simulation-based Environment

Scientific expertise requires concerted effort and the ability to overcome obstacles, but little research has addressed how these behaviors are modeled for learners in the context of simulation-based science learning. Thus, this study aimed to design instruction using cognitive modeling to promote active engagement by novice learners to ensure they feel competent to tackle novel learning problems in science. Largely drawing on work on social cognitive theory, the current study suggests the importance of a coping model, having incorrect knowledge and inadequate skills and gradually improving to a level of expertise, as an instructional aid to promote student motivation and learning in a simulation-based science learning environment. Two experimental studies were conducted with high school students in Korea who did not possess prior knowledge. Study 1 compared a Coping Model (CM) condition, where students observed a peer model who makes errors and demonstrates initial difficulties but overcomes them, to a Mastery Model (MM) condition, where students observed a peer model who presents an error-free process of interpreting information while manipulating the simulation. The CM students tended to have higher post-self-efficacy than the MM students. However, it did not change over time, nor did it differ by condition. The CM was as effective as MM for learning gains, and the CM had a more favorable impact on transfer than the MM. The CM’s negative emotions, which was intended to indicate task difficulties, may have given students an impression that the task was difficult, resulting in no increase in self-efficacy over time. Thus, Study 2 added one more condition – a coping model with affective states (CMA) – that expressed the model’s changes in emotions and motivations in addition to what the CM demonstrated, and compared its effects to the CM and MM. The CM’s emotional expressions as in Study 1 were all removed in Study 2. Findings demonstrated that self-efficacy of students increased in the CMA and CM conditions over time while self-efficacy of the MM students did not. Students in all conditions demonstrated equal learning gains, but the CMA was more effective for transfer outcomes than the MM, and the CM tended to be more effective for transfer than the MM. It is promising that a model who demonstrates difficulty in understanding but gradual progress to reach full understanding, which is the initial learning process of any novice, has potential to improve self-efficacy and promote transfer. The study discusses limitations and future study directions and concludes with implications for instructional design

The Interactions of Relationships, Interest, and Self-Efficacy in Undergraduate Physics

This collected papers dissertation explores students’ academic interactions in an active learning, introductory physics settings as they relate to the development of physics self-efficacy and interest. The motivation for this work extends from the national call to increase participation of students in the pursuit of science, technology, engineering, and mathematics (STEM) careers. Self-efficacy and interest are factors that play prominent roles in popular, evidence-based, career theories, including the Social cognitive career theory (SCCT) and the identity framework. Understanding how these constructs develop in light of the most pervasive characteristic of the active learning introductory physics classroom (i.e., peer-to-peer interactions) has implications on how students learn in a variety of introductory STEM classrooms and settings structured after constructivist and sociocultural learning theories. I collected data related to students’ in-class interactions using the tools of social network analysis (SNA). Social network analysis has recently been shown to be an effective and useful way to examine the structure of student relationships that develop in and out of STEM classrooms. This set of studies furthers the implementation of SNA as a tool to examine self-efficacy and interest formation in the active learning physics classroom. Here I represent a variety of statistical applications of SNA, including bootstrapped linear regression (Chapter 2), structural equation modeling (Chapter 3), and hierarchical linear modeling for longitudinal analyses (Chapter 4). Self-efficacy data were collected using the Sources of Self-Efficacy for Science Courses – Physics survey (SOSESC-P), and interest data were collected using the physics identity survey. Data for these studies came from the Modeling Instruction sections of Introductory Physics with Calculus offered at Florida International University in the fall of 2014 and 2015. Analyses support the idea that students’ perceptions of one another impact the development of their social network centrality, which in turn affects their self-efficacy building experiences and their overall self-efficacy. It was shown that unlike career theories that emphasize causal relationships between the development of self-efficacy and the subsequent growth of student interest, in this context student interest takes precedence before the development of student self-efficacy. This outcome also has various implications for career theories

Collective Efficacy Beliefs And Their Sources In NCAA Division I Soccer Players

Instruments for the measurement of collective efficacy beliefs in college athletes do not provide domain-specific information that reflects the unique nature of collegiate athletics or the characteristics of specific sports. Without domain-specific measures, interventions designed to enhance collective efficacy beliefs in collegiate athletes will not be optimized. This study proposes new scales to measure the collective efficacy beliefs of NCAA Division I soccer players and identify the sources of those beliefs. Additionally, this study aims to measure how well collective efficacy beliefs are predicted by their individual sources and how the academic, social, and structural background factors present in the collegiate athletic environment moderate the relationship between collective efficacy beliefs and their sources. To test collective efficacy beliefs and their sources, a survey was distributed to NCAA Division I soccer players. Scale structures were validated using a confirmatory factor analysis (CFA). The predictive power of the sources and moderating effect of background factors were analyzed with multiple regression. The results suggested a Sources of Collective Efficacy Beliefs scale comprised of positive preparation and performance environment and a Collective Efficacy Beliefs scale comprised of self-regulation and inclusivity. The results also showed that positive preparation and performance environment significantly predicted collective efficacy beliefs and background factors had no moderating effect. These results suggest that a collective efficacy beliefs scale for NCAA Division I soccer players should include academic, social, and structural items, and that coaches can enhance collective efficacy beliefs in their teams by influencing positive preparation and the performance environment

A Research Agenda for the Why, What, and How of Gamification Designs: Outcomes of an ECIS 2019 Panel

This report summarizes a panel session on gamification designs at the 2019 European Conference on Information Systems in Stockholm, Sweden. The panel explored a research agenda for gamification design. The panel considered the “what, why, and how” to analyze state-of-the-art gamification research. We present an adapted definition of gamification as one outcome of the workshop to better describe what gamification is and what it can be used for. We discuss “why” and “how” to employ gamification for different contexts. Researchers and practitioners can use the report’s research questions and insights to gamify information systems, identity outcomes that gamification concepts address, and explore new ways to gamify. Overall, we present new areas for future research and practice by identifying innovative ways to bring existing gamification concepts to a more impactful level

Community College Students\u27 Perspectives on the Use of Gamification in Online Learning

Educators are striving to use instructional methods that engage and motivate students in online coursework. Recent studies have not addressed whether the use of games as an instructional strategy is associated with improving students\u27 motivation and engagement to learn at the community college level. The purpose of this qualitative phenomenological study was to explore the lived experiences of community college students regarding motivation and engagement when taking gamified courses online. Davis\u27s technology acceptance model and Blumer\u27s interactionist model framed the study. The research questions explored a description of the lived experiences of community college students taking an online course that included gamification. Data collection was drawn from a purposive sample of 7 community college students via in-depth interviews and journal entries. Data analysis included content analysis and grounded coding to categorize information into themes. Findings showed that community college students accept gamification as an instructional strategy to learn curricular content at the community college level. Findings related to motivation and engagement will contribute to the body of knowledge to empower educators to integrate instructional strategies such as gamification as best practices in online instruction for community college students

Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes