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

Instructional Behavior and Its Impact on Student Engagement

The purpose of this quantitative study was to expand understanding of leadership behaviors and their influence on follower engagement. Researchers have shown that engagement is a predictor of retention and organizational performance. Leadership theory and the conceptual framework of worker engagement were the study\u27s theoretical anchors. Despite a proliferation of leadership studies, engagement antecedents are largely unknown. The aim of this study was to narrow the gap in the literature by examining the extent to which there may be a relationship between college instructors\u27 behaviors and student engagement. Although not traditionally regarded as frontline leaders, extant leadership literature affirmed college instructors\u27 organizational position, role, and responsibilities as direct supervisors and students as their followers. The independent variables were instructor behavior, institutional support, and depth of learning. Student engagement was the dependent variable. Correlation and regression analysis were applied to existing survey data collected in 2014 from students who were enrolled in a diverse, urban community college located in a major metropolitan city in the United States. The most prominent finding, that leadership behaviors had the strongest correlation to student engagement, contributed to the body of leadership knowledge by reaffirming leadership behaviors as a predictor of follower engagement. Given the increasing diversity of workers and followers, this study\u27s findings have the potential to help leaders more effectively engage followers who are members of historically marginalized groups, thereby, helping to narrow equity gaps and advance social justice, particularly in higher education

When, how and for whom changes in engagement happen:A transition analysis of instructional variables

The pace of our knowledge on online engagement has not been at par with our need to understand the temporal dynamics of online engagement, the transitions between engagement states, and the factors that influence a student being persistently engaged, transitioning to disengagement, or catching up and transitioning to an engaged state. Our study addresses such a gap and investigates how engagement evolves or changes over time, using a person-centered approach to identify for whom the changes happen and when. We take advantage of a novel and innovative multistate Markov model to identify what variables influence such transitions and with what magnitude, i.e., to answer the why. We use a large data set of 1428 enrollments in six courses (238 students). The findings show that online engagement changes differently —across students— and at different magnitudes —according to different instructional variables and previous engagement states. Cognitively engaging instructions helped cognitively engaged students stay engaged while negatively affecting disengaged students. Lectures —a resource that requires less mental energy— helped improve disengaged students. Such differential effects point to the different ways interventions can be applied to different groups, and how different groups may be supported. A balanced, carefully tailored approach is needed to design, intervene, or support students' engagement that takes into account the diversity of engagement states as well as the varied response magnitudes that intervention may incur across diverse students’ profiles

STUDENT PERCEPTION ON FLEXIBLE SEATING AND THEIR ENGAGEMENT: A DESCRIPTIVE RESEARCH STUDY

This descriptive study examines students’ perspectives of their engagement when seated in flexible seating with choice. The researcher analyzed interview and survey data to answer the following research questions: 1.) What are students’ perceptions of flexible seating with choice? 2.) What are students’ perceptions on their level of engagement with flexible seating with choice? The purpose of this study was achieved by interviewing and surveying first grade students who have experienced flexible seating in their Title 1 school within Southern California. Using the data from the interviews, this study identified common themes around the perception of flexible seating among randomly selected students interviewed. Under the themes of comfort, freedom of choice, focus, movement, and feelings towards flexible seating were discussed. Using the data from the surveys, this study found that 75% of student participants perceived their level of engagement to be considered true engagement or full engagement according to Schlechty’s (2011) levels of engagement, while 18.8% perceived their level to be considered strategic compliance and 6.3% perceived theirs to be retreatism. This study provides insight into how students perceive their engagement with flexible seating. The results of this study, display that flexible seating has a positive relation to engagement and an important factor of this is student choice

Detecting students who are conducting inquiry Without Thinking Fastidiously (WTF) in the Context of Microworld Learning Environments

In recent years, there has been increased interest and research on identifying the various ways that students can deviate from expected or desired patterns while using educational software. This includes research on gaming the system, player transformation, haphazard inquiry, and failure to use key features of the learning system. Detection of these sorts of behaviors has helped researchers to better understand these behaviors, thus allowing software designers to develop interventions that can remediate them and/or reduce their negative impacts on student learning. This work addresses two types of student disengagement: carelessness and a behavior we term WTF (“Without Thinking Fastidiously”) behavior. Carelessness is defined as not demonstrating a skill despite knowing it; we measured carelessness using a machine learned model. In WTF behavior, the student is interacting with the software, but their actions appear to have no relationship to the intended learning task. We discuss the detector development process, validate the detectors with human labels of the behavior, and discuss implications for understanding how and why students conduct inquiry without thinking fastidiously while learning in science inquiry microworlds. Following this work we explore the relationship between student learner characteristics and the aforementioned disengaged behaviors carelessness and WTF. Our goal was to develop a deeper understanding of which learner characteristics correlate to carelessness or WTF behavior. Our work examines three alternative methods for predicting carelessness and WTF behaviors from learner characteristics: simple correlations, k-means clustering, and decision tree rule learners

The Black Therapist-White Client Counseling Dyad: The Relationship Between Black Racial Identity and Countertransference

The racial dynamics and sociopolitical history of the United States create a unique context for the Black therapist-White client counseling dyad. Each member within this dyad may have a number of transferences or countertransferences (i.e. responses) to one another based on their racial identity and socialization experiences; all of which may affect the therapeutic process. Utilizing a mixed-method design, two research questions guided the present study: (a) Does Black racial identity predict countertransference reactions experienced by Black therapists when working with White clients? (b) What are the benefits and challenges that Black therapists self-report when working with White clients? A multivariate multiple regression analysis was proposed to examine the first research question; however, this analysis was not conducted due to an insufficiently low sample size (N=28). Therefore, a descriptive analysis of mean comparisons based on primary themes in the qualitative data was performed. On the Black Racial Identity Attitudes Scale (Helms, 1990), mean comparisons did not appear to vary significantly based on themes; however, participants generally had high scores on the Internalization subscale. On the Therapist Response Questionnaire (Betan et al., 2005), means were generally low across themes, with the exception of Positive countertransference. These results may suggest that participants in this sample had positive, stable racial identity and that these therapists enjoyed their work with White clients regardless of challenges faced. To examine the second research question, the Discovery-Oriented Approach (Mahrer, 1988) was utilized with qualitative responses from 27 therapist participants. Qualitative results highlighted 29 themes regarding the impact of racial dynamics on the counseling process. Findings from the present study highlight the benefits and challenges Black therapists encounter when working in cross-racial dyads and provide implications for multicultural training

An Inductive Method of Measuring Students’ Cognitive and Affective Processes via Self-Reports in Digital Learning Environments

Student affect can play a profoundly important role in students\u27 post-school lives. Understanding students\u27 affective states within online learning environments in particular has become an important matter of research, as digital tutoring systems have the potential to intervene at the moment that students are struggling and becoming frustrated, bored or disengaged. However, despite the importance of assessing students\u27 affective states, there is no clear consensus about what emotions are most important to assess, nor how these emotions can be best measured. This dissertation investigates students’ self-reports of their emotions and causal attributions of those emotions collected while they are solving math problems within a mathematics tutoring system. These self-reports are collected in two conditions: through limited choice Likert response and through open response text boxes. The conditions are combined with students’ cognitive attributions to describe epistemic (neither purely affective nor purely cognitive) emotions in order to explain the relationship between observable student behaviors in the MathSpring.org tutoring system and student affect. These factors include beliefs, expectations, motivations, and perceptions of ability and control. A special emphasis of this dissertation is on analyzing the role of causal attributions for the events and appraisals of the learning environment, as possible causes of student behaviors, performance, and affect

Mentoring Elements that Influence Employee Engagement

Employee disengagement is a significant issue for leaders and managers in many organizations. The general problem is the workforce in many American organizations includes disengaged employees. In 2016, only 33% of the workforce in the United States was engaged. The purpose of this quantitative study was to examine the relationship between the independent variables of mentoring, which include role modeling, acceptance and confirmation, and mentoring friendship functions with a dependent variable of employee engagement. The moderating variable of perceived organizational support was measured to test the strength or weakness of the effects that mentoring has on employee engagement. The theoretical foundation for this study was social exchange theory. The researcher recruited a convenience sample of 307 technicians and technologists representing 7 industries. The participants completed surveys and questionnaires to provide their views of mentoring, perceived organizational support, and work engagement. Data were analyzed using descriptive and inferential analysis, including Pearson\u27s correlations, linear, and stepwise regression analysis. The results of the inferential analyses indicated that each part of the mentoring variables (career support, psychosocial support, and role modeling) had an independent impact on work engagement. The interaction between psychosocial support and organizational support was also significant after accounting for the effects of mentoring and organizational support. The findings indicate that managers can achieve positive social change and improve employee well-bring within their organizations by being dutifully involved in their employees\u27 work lives. Managers should also be available to apply resources such as mentoring for technicians and technologist when needed

Therapist Self-Reported Attachment Organization and Countertransference Responses to Psychotherapy Clients

Therapists experience thoughts, feelings, and behaviors in response to their clients, which are sometimes referred to as countertransference. Such responses may be influenced by the therapist’s personal history, including the quality of their attachment experiences. Research has demonstrated that adult attachment organizations influence a person’s cognitive, behavioral, and affective responses toward close others, thus providing a useful framework for understanding some countertransference experiences of therapists. This quantitative study sought to add to the existing literature by examining the relationship between therapist self-reported attachment organization and countertransference responses to clients. Seventy-three therapists participated in this study, including licensed psychologists, doctorate-level psychologists, and psychologists-in-training. Results of this study found that therapist self-reported attachment anxiety and avoidance are associated with a range of countertransference responses to clients. Specifically, attachment-related anxiety was positively correlated with overwhelmed/disorganized countertransference and was a significant predictor of helpless/inadequate, disengaged, and criticized/mistreated countertransference responses. Attachment-related avoidance was positively correlated with overwhelmed/disorganized and disengaged countertransference responses, but was not a significant predictor of any countertransference response types. Additionally, attachment-related security was not associated with any countertransference response types. This study expands existing literature for understanding the relationship between therapist attachment and countertransference, and provides a novel use for the Therapist Response Questionnaire, as this measure has not yet been utilized when examining therapist attachment and countertransference. The general direction of the findings have clinical implications for psychotherapy practitioners, suggesting that therapists may benefit from developing and maintaining an awareness of the potential influence of their attachment history

Towards Student Engagement Analytics: Applying Machine Learning to Student Posts in Online Lecture Videos

The use of online learning environments in higher education is becoming ever more prevalent with the inception of MOOCs (Massive Open Online Courses) and the increase in online and flipped courses at universities. Although the online systems used to deliver course content make education more accessible, students often express frustration with the lack of assistance during online lecture videos. Instructors express concern that students are not engaging with the course material in online environments, and rely on affordances within these systems to figure out what students are doing. With many online learning environments storing log data about students usage of these systems, research into learning analytics, the measurement, collection, analysis, and reporting data about learning and their contexts, can help inform instructors about student learning in the online context. This thesis aims to lay the groundwork for learning analytics that provide instructors high-level student engagement data in online learning environments. Recent research has shown that instructors using these systems are concerned about their lack of awareness about student engagement, and educational psychology has shown that engagement is necessary for student success. Specifically, this thesis explores the feasibility of applying machine learning to categorize student posts by their level of engagement. These engagement categories are derived from the ICAP framework, which categorizes overt student behaviors into four tiers of engagement: Interactive, Constructive, Active, and Passive. Contributions include showing what natural language features are most indicative of engagement, exploring whether this machine learning method can be generalized to many courses, and using previous research to develop mockups of what analytics using data from this machine learning method might look like