The problem of this study was to determine if at-risk students who were enrolled in an educational support class for one hour a day would have an improvement on the four at-risk indicators being measured over students not enrolled in the academic support program. The four at-risk indicators are grade point average, self-concept, days absent from school, and discipline referrals. The hypothesis formulated for this study predicted no significant difference in mean scores of the four measured indicators between groups. These indicators were measured by the Piers-Harris Children's Self-Concept Scale, official school attendance records, official school transcripts, and the school's discipline records book. The at-risk population was identified from the use of an at-risk indicator scale. After random placement into either the control or experimental groups the samples were divided and analyzed according to grade and gender. The study was conducted over a 12 week period and included students from the Memphis, Michigan School District in grades six through nine. Data were analyzed by the independent means t test at the .05 level. The experimental group means were further analyzed for practical significance and for directional improvement. A series of tables provides a comparison of scores for all students participating in the study. For students participating in the experimental group three of the four indicators, self-esteem, days absent from school, and grade point average had a statistically significant difference in mean scores. The majority of mean scores moved in a direction of improvement indicating enrollment in the treatment had a positive influence on the at-risk indicators. Most scores that did not show a statistically significant difference in means did report a high level of practical significance that was a result of being enrolled in the academic support program.

This study was conducted to analyze the performance differences between alternative education campuses in Texas that used teacher-directed strategies and those that used self-directed strategies. The study was also conducted to inform educators of the results these two strategies had achieved with at-risk students during the three years of 2006-2008. The study used the results from the Texas Assessment of Knowledge and Skills test as reported in the AEIS annual reports from the Texas Education Agency. Alternative education schools were grouped according to the strategy used to educate at-risk students. The results of the statistical tests showed the two strategies had similar performance results and there was no statistical difference between the two. The results offered several implications concerning the ability of at-risk students to achieve in alternative education schools including possible reasons why students who were previously unsuccessful became successful in alternative settings. The report also addressed the number of students who continued to be unsuccessful even when placed on an alternative education campus. Possible reasons for this continued inability to succeed are discussed. Recommendations for further research were listed at the conclusion of the study.

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Educators in the United States (U.S.) are increasingly turning to intelligent tutoring systems (ITS) to provide differentiated math instruction to high school students. However, many struggling high school learners do not perform well on these platforms, which reinforces the need for more awareness about effective supports that influence the achievement of learners in these milieus. The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners' performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. The theoretical framework consisted of knowledge base theory, the zone of proximal development, and cognitive learning theory. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics.

All 7 high schools located in a school district in Alabama have experienced a high dropout rate since 2012. The purpose of this qualitative descriptive study was to understand the perceptions of recent high school dropouts about support systems that could have assisted them in completing the requirements to receive a high school diploma. Research questions centered on recent high school dropouts' views on what supports from home and from teachers they could have received to assist them in completing high school and what things they could have done differently to receive their high school diplomas. Bandura's theories of self-efficacy and social learning served as the conceptual framework for this study. Interview data were collected from 10 participants who were selected via purposive sampling from high schools in the Baldwin County school district's local GED program. Data were analyzed using Hatch's 9 step typology for open coding. All of the participants said that they had dropped out or quit school for a variety of reasons, including a change in program, family responsibilities, loss of interest, or to get a job. They reported feeling that their parents could have done more to keep them from dropping out. Only half of the participants said they had received support from teachers. Most participants reported feeling that they themselves could have done something more to complete high school. The results of this study could lead to positive social change as parents and teachers become more aware of how to support students at risk of dropping out and the impact this can have on their communities.

Student attrition is one of the long-standing problems facing higher education institutions despite the extensive research that has been undertaken to address it. To increase students’ success and retention rates, there is a need for early alert systems that facilitate the identification of at-risk students so that remedial measures may be taken in time to reduce the risk. However, incorporating ML predictive models into early warning systems face two main challenges: improving the accuracy of timely predictions and the generalizability of predictive models across on-campus and online courses. The goal of this study was to develop and evaluate predictive models that can be applied to on-campus and online courses to predict at-risk students based on data collected from different stages of a course: start of the course, 4th week, 8th week, and 12th week. In this research, several supervised machine learning algorithms were trained and evaluated on their performance. This study compared the performance of single classifiers (Logistic Regression, Decision Trees, Naïve Bayes, and Artificial Neural Networks) and ensemble classifiers (using bagging and boosting techniques). Their performance was evaluated in term of sensitivity, specificity, and Area Under Curve (AUC). A total of four experiments were conducted based on data collected from different stages of the course. In the first experiment, the classification algorithms were trained and evaluated based on data collected before the beginning of the semester. In the second experiment, the classification algorithms were trained and evaluated based on week-four data. Similarly, in the third and fourth experiments, the classification algorithms were trained and evaluated based on week-eight and week-12 data. The results demonstrated that ensemble classifiers were able to achieve the highest classification performance in all experiments. Additionally, the results of the generalizability analysis showed that the predictive models were able to attain a similar performance when used to classify on-campus and online students. Moreover, the Extreme Gradient Boosting (XGBoost) classifier was found to be the best performing classifier suited for the at-risk students’ classification problem and was able to achieve an AUC of ≈ 0.89, a sensitivity of ≈ 0.81, and specificity of ≈ 0.81 using data available at the start of a course. Finally, the XGBoost classifier was able to improve by 1% for each subsequent four weeks dataset reaching an AUC of ≈ 0.92, a sensitivity of ≈ 0.84, and specificity of ≈ 0.84 by week 12. While the additional learning management system's (LMS) data helped in improving the prediction accuracy consistently as the course progresses, the improvement was marginal. Such findings suggest that the predictive models can be used to identify at-risk students even in courses that do not make significant use of LMS. The results of this research demonstrated the usefulness and effectiveness of ML techniques for early identification of at-risk students. Interestingly, it was found that fairly reliable predictions can be made at the start of the semester, which is significant in that help can be provided to at-risk students even before the course starts. Finally, it is hoped that the results of this study advance the understanding of the appropriateness and effectiveness of ML techniques when used for early identification of at-risk students.

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Students without caring, positive role models often make poor decisions. School personnel are aware of the need to help these students be productive members of society; therefore, they examine strategies and reforms to reach them. A mentoring program is one such intervention that is gaining in popularity. This research study examined a mentoring program entitled the LISTEN (Linking Individual Students To Educational Needs) Mentoring Program that I developed in 2003. For the purposes of this research, the mentoring program was developed and implemented in one middle school in Northeast Tennessee. The goal of the LISTEN mentoring program was to identify at-risk students and provide them with positive adult role models, who were not necessarily their classroom teachers. The mentors worked with the students to assist in developing positive behaviors and better decision making skills. The implementation of LISTEN was assessed throughout this study. The second component of the investigation focused on program perceptions by teachers and students. The final component of this research centered on recommendations for improving the program and enhancing the program's components for further development. This experimental study analyzed archival data from 2004-2005 to determine the effects of the LISTEN mentoring program on identified at-risk students in grades 6 through 8 in a Northeast Tennessee middle school. Specifically, the study investigated the effects of a mentor program on students' grade-point average, discipline referrals, and attendance records. Findings indicated that there were significant differences in students' grade-point averages, school attendance, and discipline referrals from 1 school year to the next among students who participated in the LISTEN mentor program. Students' grade-point averages increased significantly from 2003-2004 to 2004-2005 for 5 of the 6 six-week grading periods and for the entire year. Mean numbers of student discipline referrals and days absent decreased significantly for 5 of the 6 six-week grading periods from 2003-2004 to 2004-2005 and for the entire year. Contrary to typical at-risk behavior, this study showed that 54 of the original 57 participants returned to the school in the 2004-2005 school year, while only 3 students transferred to other schools.

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