No description available.

A team of graduate students from Brigham Young University under the supervision of the main author, Dr. Henrichsen, collaborated on creating a book with an accompanying website, Basic Training and Resources for Teaching English to Speakers of Other Languages (BTRTESOL). The entire program consists of 10 main sections with nearly 50 units addressing topics that novice teachers can choose from to help them prepare to teach English to non-native speakers. The BTRTESOL program answers the need for material designed for novice teachers, material that will help them to be better prepared in a minimalistic, non-overwhelming way. The goal of this program is that novice teachers will be more prepared to face the challenges and responsibilities that teaching English to speakers of other languages (TESOL) brings. Each member of the BTRTESOL team created two units of the program. Each unit summarizes the information about each topic into a few pages of text that provides the minimum amount of information novices should know. The units then direct them to other sources in order to learn more. The program is still under development and is anticipated to be completed within the next few years with other TESOL MA students and Dr. Henrichsen completing subsequent units. This selected project details the making of two units of the BTRTESOL program, one in section one, "Basic Concepts," titled "Tutoring Guidelines," and one in section two, "Designing Programs and Lessons," titled "Designing an Overall Plan for a Course." The tutoring unit describes some guidelines for novice tutors, whether or not they have taught larger classes before. The course design unit describes the basics of creating a course curriculum. Novice tutors and teachers are frequently expected to plan everything for the tutoring sessions or courses as soon as they start their assignments. These units are designed to give them a starting point. In addition to an opening scenario, explanatory text, and resource connections, both of these units provide activities (i.e., a video clip or case studies) for visualization, analysis, reflection, and practical application.

This thesis hypothesizes that a method for selecting instructional strategies (specifically media) based in part on a relationship between learning style preference and personality preference provides more relevant and understandable feedback to students and thereby higher learning effectiveness. This research investigates whether personality preferences are valid predictors of learning style preferences. Since learning style preferences are a key consideration in instructional strategies and instructional strategies are a key consideration in learning effectiveness, this thesis contributes to a greater understanding of the relationship between personality preferences and effective learning in intelligent tutoring systems (ITS). This research attempts to contribute to the goal of a "truly adaptive ITS" by first examining relationships between personality preferences and learning style preferences; and then by modeling the influences of personality on learning strategies to optimize feedback for each student. This thesis explores the general question "what can personality preferences contribute to learning in intelligent tutoring systems?" So, why is it important to evaluate the relationship between personality preferences and learning strategies in ITS? "While one-on-one human tutoring is still superior to ITS in general, this approach is idiosyncratic and not feasible to deliver to [any large population] in any cost-effective manner." (Loftin, 2004). Given the need for ITS in large, distributed populations (i.e. the United States Army), it is important to explore methods of increasing ITS performance and adaptability. Findings of this research include that the null hypothesis that "there is no dependency between personality preference variables and learning style preference variables" was partly rejected. Highly significant correlations between the personality preferences, openness and extraversion, were established for both the active-reflective and sensing-intuitive learning style preferences. Discussion of other relationships is provided.

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student's affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student's interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student's mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle's (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank's (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables.

This paper sought to accomplish three goals. First, it provided a systematic, comparative review of several intelligent tutoring systems (ITS). Second, it summarized problems and solutions presented and solved by developers of ITS by consolidating the knowledge of the field into a single review. Third, it provided a unified language from which ITS can be reviewed and understood in the same context. The findings of this review centered on the 5-Component Framework. The first component, the domain model, showed that most ITS are focused on science, technology, and mathematics. Within these fields, ITS generally have mastery learning as the desired level of understanding. The second component, the tutor model, showed that constructivism is the theoretical strategy that informs most ITS. The tutoring tactics employed in the ITS stem from this paradigm. The third component, the student model, describes the several ways ITS infer what a student knows. It described the variety of data that is collected by an ITS and how it is used to build the student model. The fourth component, the interface, revealed that most ITS are now web-based, but vary in their capacity to interact with students. It also showed that user experience is underreported and ought to be included more in the research. Finally, the fifth component, learning gains, demonstrated that ITS are capable of producing learning gains equivalent to a human tutor. However, reporting learning gains does not seem to be a focus of the literature.

Chinese business leaders suffered from the crisis of COVID-19 and the Double Reduction Policy, and used various strategies and learning practices to survive the crisis. The purpose of this modified exploratory multicase study was to explore how leaders in the Chinese tutoring industry made sense of the crisis of the Pandemic and the Double Reduction Policy and learned to think strategically in a time of crisis. The study not only uncovered how Chinese business leaders used different strategies to deal with a crisis and learned to think strategically while adapting to the new environment but also brought implications and insights to business leaders about effective strategies and learning practices to cultivate strategic thinking in a complex and fast-changing world. The study addressed the following four research questions: 1.How did the business leaders in tutoring companies make sense of the complexities of the crisis of COVID-19 and the Double Reduction Policy? 2.What strategies, if any, did the business leaders develop to deal with the crisis? 3.In what ways, if at all, did the business leaders learn to think strategically while dealing with challenges? 4.What other factors helped or hindered the business leaders’ learning to think strategically in a time of crisis? Qualitative semi-structured interviews (critical incident interviews included), surveys, and a focus group discussion were used to collect data from 15 Chinese business leaders from the tutoring industry. The study generated four findings: Finding 1: The crisis negatively impacted the participants and their companies at different levels, but it also served as a valuable learning opportunity for their long-term development. Finding 2: The participants developed strategies to deal with the crisis at personal, organizational, and social levels. Finding 3: The participants learned to think strategically from direct experiences, indirect experiences, and two thinking processes—systems thinking and metaphorical thinking. Finding 4: Policies and relationships were two outstanding factors that hindered or helped their learning to think strategically. These findings indicated that (1) unprecedented crises like COVID-19 and the Double Reduction Policy can bring people benefits and valuable insights, (2) business leaders can develop critical strategies by combining their own and others' experiences and strategic insights into dealing with crises, (3) business leaders can use informal learning practices and deliberately use them to cultivate their strategic thinking, and (4) business leaders need to combine learning and action for cultivating strategic thinking capabilities. Lastly, business leaders should consider the influence of policies and relationships in their strategy formulation and learning process.

General Chemistry II is a common chemistry course that is required for professional school such as, medical, dental, optometry schooling. Considering that it historically has a high drop, fail, withdrawal rate, it is surprising that less than 10% of students in chemistry II attend tutoring at the Student Academic Resource Center (SARC). In this tutoring center, sessions are led by students who have excelled previously in that specific course. The objective of this research is to investigate the relationship between student motivation and attendance in SARC peer tutoring sessions for chemistry II. More precisely, to better understand the connection between those who have a motivation of getting a good grade and learning the material and those who attend tutoring. In order to gain insight on a student’s motivation to attend sessions in SARC, a survey was distributed to those taking the class currently. Two modes were used including paper survey and online. Findings from this investigation will lead to suggestions to increase SARC chemistry tutoring attendance which could positively impact the success of STEM students on UCF's campus.

This study investigated the academic progress of special admit football players compared to general admit football players at a private, Division I university in the western United States. Using mixed methods, the researcher quantitatively compared the two groups in terms of credits enrolled, credits attained, GPA and progress toward degree. The researcher also interviewed special admit football players to determine their perceptions of academic support service. Data answer the quantitative and qualitative study questions. Most notably, the researcher found that, while general admit football players had higher GPAs than the special admit athletes, the special admit athletes experienced the same amount of progress toward degree as their regular admit teammates. Furthermore, participant interviews revealed that athletes most value the learning specialists and tutors within the university’s support service framework Discussion includes implications for practice.

No description available.

No description available.