Understanding and Supporting Vocabulary Learners via Machine Learning on Behavioral and Linguistic Data

This dissertation presents various machine learning applications for predicting different cognitive states of students while they are using a vocabulary tutoring system, DSCoVAR. We conduct four studies, each of which includes a comprehensive analysis of behavioral and linguistic data and provides data-driven evidence for designing personalized features for the system. The first study presents how behavioral and linguistic interactions from the vocabulary tutoring system can be used to predict students' off-task states. The study identifies which predictive features from interaction signals are more important and examines different types of off-task behaviors. The second study investigates how to automatically evaluate students' partial word knowledge from open-ended responses to definition questions. We present a technique that augments modern word-embedding techniques with a classic semantic differential scaling method from cognitive psychology. We then use this interpretable semantic scale method for predicting students' short- and long-term learning. The third and fourth studies show how to develop a model that can generate more efficient training curricula for both human and machine vocabulary learners. The third study illustrates a deep-learning model to score sentences for a contextual vocabulary learning curriculum. We use pre-trained language models, such as ELMo or BERT, and an additional attention layer to capture how the context words are less or more important with respect to the meaning of the target word. The fourth study examines how the contextual informativeness model, originally designed to develop curricula for human vocabulary learning, can also be used for developing curricula for various word embedding models. We identify sentences predicted as low informative for human learners are also less helpful for machine learning algorithms. Having a rich understanding of user behaviors, responses, and learning stimuli is imperative to develop an intelligent online system. Our studies demonstrate interpretable methods with cross-disciplinary approaches to understand various cognitive states of students during learning. The analysis results provide data-driven evidence for designing personalized features that can maximize learning outcomes. Datasets we collected from the studies will be shared publicly to promote future studies related to online tutoring systems. And these findings can also be applied to represent different user states observed in other online systems. In the future, we believe our findings can help to implement a more personalized vocabulary learning system, to develop a system that uses non-English texts or different types of inputs, and to investigate how the machine learning outputs interact with students.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162999/1/sjnam_1.pd

Finding the fit: job Crafting as a Leadership Tool

Too many people go their entire lives in jobs that aren\u27t quite the right fit. In fact, currently 70% of the American workforce is disengaged, a symptom of people feeling like the position or career they are in is not right for them. Some people end up switching jobs or careers multiple times throughout their life, while others may remain in a job they dislike. The focus of my research was to look at a third possible solution for unhappy workers: job crafting. By giving workers the freedom to add new tasks or alter existing ones, expand or contract their social networks, or reframe the purpose and meaning of their work, they can gain a fresh perspective on the work they do and align their strengths and interests with the objectives of their job. I explored this idea by doing a quantitative and qualitative study on the intersection of perceived level of job crafting and self-reported job satisfaction. The results showed that there was a significant positive correlation between the two variables. Qualitative analysis revealed that many participants were already engaging in job crafting behaviors and agreed that being able to job craft would increase their engagement at work. Ultimately, this study was conducted to provide guidance and recommendations to leaders and management in business. Job crafting appears to be a viable solution for retaining talent and reengaging the workforce

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

Under-represented Students\u27 Engagement in Secondary Science Learning: a Non-equivalent Control Group Design

Problem. A significant segment of the U.S. population, under-represented students, is under-engaged or disengaged in secondary science education. International and national assessments and various research studies illuminate the problem and/or the disparity between students’ aspirations in science and the means they have to achieve them. To improve engagement and address inequities among these students, more contemporary and/or inclusive pedagogy is recommended. More specifically, multicultural science education has been suggested as a potential strategy for increased equity so that all learners have access to and are readily engaged in quality science education. While multicultural science education emphasizes the integration of students’ backgrounds and experiences with science learning , multimedia has been suggested as a way to integrate the fundamentals of multicultural education into learning for increased engagement. In addition, individual characteristics such as race, sex, academic track and grades were considered. Therefore, this study examined the impact of multicultural science education, multimedia, and individual characteristics on under-represented students’ engagement in secondary science. Method. The Under-represented Students Engagement in Science Survey (USESS), an adaptation of the High School Survey of Student Engagement, was used with 76 highschool participants. The USESS was used to collect pretest and posttest data concerning their types and levels of student engagement. Levels of engagement were measured with Strongly Agree ranked as 5, down to Strongly Disagree ranked at 1. Participants provided this feedback prior to and after having interacted with either the multicultural or the nonmulticultural version of the multimedia science curriculum. Descriptive statistics for the study’s participants and the survey items, as well as Cronbach’s alpha coefficient for internal consistency reliability with respect to the survey subscales, were conducted. The reliability results prompted exploratory factory analyses, which resulted in two of the three subscale factors, cognitive and behavioral, being retained. One-within one-between subjects ANOVAs, independent samples t-test, and multiple linear regressions were also used to examine the impact of a multicultural science education, multimedia, and individual characteristics on students’ engagement in science learning. Results. There were main effects found within subjects on posttest scores for the cognitive and behavioral subscales of student engagement. Both groups, using their respective versions of the multimedia science curriculum, reported increased engagement in science learning. There was also a statistical difference found for the experimental group at posttest on the measure of “online science was more interesting than school science.” All five items unique to the posttest related to the multimedia variable were found to be significant predictors of cognitive and/or behavioral engagement. Conclusions. Engagement in science learning increased for both groups of participants; this finding is aligned with other significant research findings that more embracive and relevant pedagogies can potentially benefit all students. The significant difference found for the experimental group in relation to the multimedia usage was moderate and also may have reflected positive responses to other questions about the use of technology in science learning. As all five measures of multimedia usage were found to be significant predictors of student engagement in science learning, the indications were that: (a) technical difficulties did not impede engagement; (b) participants were better able to understand and visualize the physics concepts as they were presented in a variety of ways; (c) participants’ abilities to use computers supported engagement; (d) participants in both groups found the online science curriculum more interesting compared to school science learning; and (e) the ability to immediately see the results of their work increased engagement in science learning

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

The Effect of Universal Design for Learning on Student Engagement and Achievement in a Southwest Missouri School District

The researcher’s primary purpose in this mixed-method study was to investigate if teachers who applied Universal Design for Learning (UDL) to a unit of study noted increased engagement in students overall and in teacher-identified students considered “disengaged” compared to students in the same unit of study without the UDL methods applied. Additionally, the researcher sought to determine if students in a UDL course had more significant achievement than students in the same unit of study without the UDL framework applied. Primarily, the researcher aimed to ascertain if UDL would benefit the participating district\u27s students, particularly those not utilizing special services. Participants were asked to complete a three-hour course over UDL before the study began. Via a qualitative survey, teachers answered open-ended questions allowing data analysis that included defined themes of what student engagement and disengagement entailed. Additionally, the researcher asked teachers to categorize their students within their UDL and non-UDL classrooms into one of two categories: engaged or disengaged. The researcher used quantitative analysis to determine if there were differences in achievement between disengaged students in the UDL environment versus the non-UDL environment and differences in achievement between engaged students in both settings. The researcher used a Likert-type question format pre- and post-study via survey to determine growth in teacher mindset regarding student and teacher efficacy. The results of this study were mixed. While the use of UDL did not show a difference in the increase in student scores from pre- to post-unit, there were positive results from training and the use of UDL in classrooms. Teachers saw increased student engagement and participation, increased enjoyment of content from students, increased mindsets regarding students, and increased confidence in their abilities to help all learners. This study demonstrated that UDL could increase engagement in students who had been previously identified as disengaged. It cannot be determined if UDL impacted student scores because the quantitative data showed similar scores between UDL and non-UDL classes. The size of this study and the fact that it was designed around a convenience sample of teachers limits its application beyond the district where the study was completed. That said, the results provide a positive implication for bigger studies with a broader scope of participants. Universal Design for Learning is a successful framework. This study only further demonstrates its success in reaching students outside the scope of special services for which research is lacking

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

The Influence Of Concept-Based Instruction On Student Academic Engagement

Student engagement, including behavior, academic, cognitive, and social/emotional engagement is a complex multidimensional element of students’ overall well-being and success in school. As dropout rates continue to increase along with students feeling disenfranchised from the current educational system, there is a deep need to improve students’ experience in school by having them engaged in their learning. The purpose of this qualitative study is to identify the influence, if any, of concept-based curriculum and instruction on student academic engagement. This study examined the multiple elements of student academic engagement by addressing the concept-based learning construct, and student ownership of their learning and inquiry while simultaneously unpacking the connection between these elements of learning and brain-based learning. The research was conducted through one-on-one semi-structured interviews with a global network of international teachers who provided feedback from a variety of school settings and geographical locations on student engagement and concept-based instructional practices. The findings of the study uncovered the following six emerging themes, student-ownership of their learning, student choice, inquiry, positive relationships and rapport, monitoring student learning, and assessment practices. The recommendations for action in the study include providing training for teachers and administrators on how the brain learns new information and instructional strategies to support how the brain learns, balancing content and standards coverage with meaningful learning experiences, and allowing teachers the needed time to build strong relationships and rapport with their students

What Is the Relationship between Employee Engagement and Job Satisfaction to Organizational Commitment in Supply Chain Management? An Examination of These Causal Factors in Supply Chain Managers in the Middle East

In the Middle East, one of the biggest challenges facing supply chain business leaders was the expatriate assignment failures, an average turnover rate of 12%, with the main reason an inability to cope with lifestyle adjustments due to cultural and family pressures. Additionally, fifty-one percent of expatriates leave the multinational corporation (MNC) within two years of repatriation (De Ruiter, Lub, Jansma, and Blomme, 2018; Haak-Saheem, Darwish, & Al-Nasser, 2017; Stoermer, Haslberger, Froese, & Kraeh, 2018). The Middle Eastern workforce showed the disproportionate ratio of expatriates to local nationals in which nearly 99% of employees in the private sector in the United Arab Emirates (UAE) were expatriates (De Ruiter et al., 2018 & Haak-Saheem et al., 2017). The purpose of this quantitative correlation study is to examine the relationship between independent variables (employee engagement-EE and job satisfaction-JS), and dependent variable (organizational commitment-OC) in the supply chain managers of the Middle East organization. The findings of this study showed that there are statistically significant relationships among EE, JS, and OC, indicating the p-values lower than the alpha level of 0.05 for all variables. The findings also showed that age does have a moderating effect on the relationship between EE and OC, Beta = 0.115, p = 0.049, but not in the relationship between JS and OC. Conversely, gender, nationality, and tenure showed no moderation effect in the relationships among EE, JS, and OC. Recommendations to increase both EE and JS are (1) increase EE through organizational culture, gender-balanced management teams, self-efficacy, work-life balance, and a supportive work environment, and (2) increase JS through effective communication, employee development, rewards and recognition, and supervisor support