Development of a system architecture for the prediction of student success using machine learning techniques

“ The goals of higher education have evolved through time based on the impact that technology development and industry have on productivity. Nowadays, jobs demand increased technical skills, and the supply of prepared personnel to assume those jobs is insufficient. The system of higher education needs to evaluate their practices to realize the potential of cultivating an educated and technically skilled workforce. Currently, completion rates at universities are too low to accomplish the aim of closing the workforce gap. Recent reports indicate that 40 percent of freshman at four-year public colleges will not graduate, and rates of completion are even lower for community colleges. Some efforts have been made to adjust admission requirements and develop systems of support for different segments of students; however, completion rates are still considered low. Therefore, new strategies need to consider student success as part of the institutional culture based on the information technology support. Also, it is key that the models that evaluate student success can be scalable to other higher education institutions. In recent years machine learning techniques have proven to be effective for such purpose. Then, the primary objective of this research is to develop an integrated system that allows for the application of machine learning for student success prediction. The proposed system was evaluated to determine the accuracy of student success predictions using several machine learning techniques such as decision trees, neural networks, support vector machines, and random forest. The research outcomes offer an important understanding about how to develop a more efficient and responsive system to support students to complete their educational goals”--Abstract, page iv

The Use of Semester Course Data for Machine Learning Prediction of College Dropout Rates

Predicting those at-risk of dropping out allows schools to assist students before it happens. Machine learning (ML) techniques can predict the likelihood of students completing a course, enrolling in future semesters, or graduating from college. This study compares four ML techniques to predict dropout rates using a student’s demographic information and performance in individual courses over all semesters enrolled. Using ten semester models the logistic regression method had the best accuracy of 84.8% versus decision trees (82.2%), neural networks (80.8%), and support vector machines (72.5%). The semester course performance data is a useful input for predicting dropout rates

Using ensemble decision tree model to predict student dropout in computing science

Science, Technology, Engineering and Mathematics (STEM) professionals play a key role in the development of an economy. STEM workers are critical thinkers as they contribute immensely by driving innovations. There is a high demand for professionals in the STEM fields but there is also a shortage of human resource in these areas. One way to reduce this problem is by identifying students who are at-risk of dropping out and then intervening with focused strategies that will ensure that these students remain in same the programme till graduation. Therefore, this research aims to use a data mining classification technique to identify students who are at-risk of dropping out from their Computing Science (CS) degree programmes. The Random Forest (RF) decision tree algorithm is used to learn patterns from historical data about first-year undergraduate CS students who are enrolled in a tertiary institute in the South Pacific. A number of factors are used which comprise of students demographic information, previous education background, financial information as well as data about students' academic interaction. Feature selection is performed to determine which factors have greater influence in students' decision in dropping out. Cross-validation techniques are used to ensure that the models are not over-fitted. Two models were built using a 5fold and 10-fold cross-validation and the results were compared using several measures of model performance. The results show that the factors corresponding to students' academic performance in a first-year programming course had the greatest impact student attrition in CS

Machine Learning Applications in Graduation Prediction at the University of Nevada, Las Vegas

Graduation rates of four-year institutions are an increasingly important metric to incoming students and for ranking universities. To increase completion rates, universities must analyze available student data to understand trends and factors leading to graduation. Using predictive modeling, incoming students can be assessed as to their likelihood of completing a degree. If students are predicted to be most likely to drop out, interventions can be enacted to increase retention and completion rates. At the University of Nevada, Las Vegas (UNLV), four-year graduation rates are 15% and six-year graduation rates are 39%. To improve these rates, we have gathered seven years worth of data on UNLV students who began in the fall 2010 semester or later up to the summer of 2017 which includes information from admissions applications, financial aid, and first year academic performance. The student group which is reported federally are first-time, full-time freshmen beginning in the summer or fall. Our data set includes all freshmen and transfer students within the time frame who meet our criteria. We applied data analysis and visualization techniques to understand and interpret this data set of 16,074 student profiles for actionable results by higher education staff and faculty. Predictive modeling such as logistic regression, decision trees, support vector machines, and neural networks are applied to predict whether a student will graduate. In this analysis, decision trees give the best performance

An Illustration of Using Adaptive Data Mining to Develop Strategic Knowledge Bases for Student Retention

Technological development has engaged educational institutions in fierce global competition. To be competitive in meeting the changing needs of today’s student population, educational institutions find it imperative to prioritize student retention efforts and to develop strategies that interact with students to effectively provide additional value and service. In this study we developed a two-module system: a decision tree for predicting a student’s decision to stay until graduation and an affinity analysis algorithm for identifying the relationship between student attributes and student decisions. We followed a three-phase-six-stage adaptive data mining cycle in developing a knowledge base for student retention strategies. The affinity analysis initially identified more than 400 association relationships with student retention. By applying inductive inference, the association rule set was refined iteratively down to less than 30 rules, and useful strategic implications were developed regarding how the selected factors were associated with a student’s decision. This set of implications and factors was then integrated into the development of strategies for student retention

The Economics & Psychology of Inequality and Human Development

Recent research on the economics of human development deepens under- standing of the origins of inequality and excellence. It draws on and contributes to personality psychology and the psychology of human development. Inequal- ities in family environments and investments in children are substantial. They causally aect the development of capabilities. Both cognitive and noncognitive capabilities determine success in life but to varying degrees for dierent out- comes. An empirically determined technology of capability formation reveals that capabilities are self-productive and cross-fertilizing and can be enhanced by investment. Investments in capabilities are relatively more productive at some stages of a child's life cycle than others. Optimal child investment strategies dier depending on target outcomes of interest and on the nature of adversity in a child's early years. For some congurations of early disadvantage and for some desired outcomes, it is ecient to invest relatively more in the later years of childhood than in the early years.inequality, capabilities, noncognitive traits, human development, technology of capability formation, policy targeting

The Relationship of Perceived Learning and Self-Regulated Learning of Undergraduate Students and the Curiosity Scores Generated by Packback

Institutions work to improve their retention rates. Research supports academically and socially integrated students are more likely to develop a commitment to the institution and persist to graduation. Historically these theories emphasized perceived learning and self-regulated learning as contributing factors for student retention. Curiosity is a motivational factor that improves student engagement and academic integration. Discussion boards are used with face-to-face, online, and hybrid courses. Instructors use the virtual workspace to build a collaborative community for students to engage with one another, the instructor, and the course material. Packback uses artificial intelligence (AI) to heighten student engagement on discussion board posts by providing immediate feedback to students and publishing a leader board with curiosity scores. Through the lens of Connectivism and the Community of Inquiry Model for online learning, this predictive correlational study explored the relationship of perceived learning and self-regulated learning of students enrolled in an undergraduate political science course and the curiosity score generated by Packback. The study involved a convenience sample from a land grant institution located in the southeastern United States . The Cognitive, Affective, and Psychomotor (CAP) survey measured perceived learning using a seven-point Likert scale. The Online Self-Regulated Learning Questionnaire (OSLQ) measured self-regulated learning behaviors using a five-point Likert scale. Packback’s Curiosity Score is generated through an algorithm using presentation, credibility, and effort. A multiple regression analysis demonstrated a lack of sufficient evidence to support a predictive relationship between perceived learning and self-regulated learning (predictor variables) upon curiosity scores (criterion variable) generated by Packback

Academic Performance Among First-Year College Freshmen Following Participation in a Summer Bridge Program

The primary purpose of this study was to determine the differences in the academic outcomes of first-year academically underprepared TN Promise-eligible college freshmen who participated in a college bridge program. A comparative research design was applied to existing data, including first-semester GPA, first-semester credit completion rate, first college-level mathematics course GPA, first college-level English course GPA, and fall-to-fall persistence rates. A random sample of 412 first-time freshman college students from five cohorts was analyzed using descriptive statistics for eight research questions. These findings indicated that there were no significant differences among college bridge participants and non-bridge participants. Non-bridge program participants performed slightly better than bridge program participants for all research questions, including first-semester GPA, first-semester credit completion rate, first English course GPA, and first mathematics course GPA. Similar results were also found for research questions that analyzed underrepresented participants. However, despite finding that non-bridge participants achieved minor but consistently higher performance outcomes, the fall-to-fall persistence rates for bridge participants and non-bridge participants were nearly identical. Additional analyses indicated that low-income bridge participants slightly outperformed their low-income non-bridge peers in first-semester GPA and credit completion rate, and first-generation bridge program participants and first-generation non-bridge participants performed almost identically, though no statistical significance was found. This study documented the short-term academic effects that college bridge programs can have on academically underprepared college freshmen. These findings resemble similar findings from existing bridge program research that likewise did not find improvements in student performance or outcomes. Additionally, this study along with ambiguous findings from previous research, might indicate that bridge program efficacy is highly reliant on program design, purpose, and target populations, and the concept is not a universal approach to prepare students academically and socially for the curricular expectations of postsecondary education. Implications for future research and recommendations for policymakers are discussed

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

The Effects of Rotational Blended Learning on Course Grades in High School Credit Recovery Math I and English I Courses

Despite the increasing popularity of using online and blended technology to recover lost initial credit, there has been limited research as to the effectiveness of online only credit recovery models, and the effectiveness of blended learning models, especially in secondary public education. This study is important in that it analyzes which method of content delivery is most effective for a particular population. The purpose of this causal-comparative study was to determine if there were any statistical differences in the individual final numerical course grades of students taking online only credit recovery English I and Math I classes, and students taking the same credit recovery classes in a rotational blended learning environment. This study used an independent samples t-test, and descriptive statistics to compare archival data from high school students in a rural North Carolina county who took online only, or blended credit recovery classes, during the 2017-2018 and 2018-2019 academic years. After the t-test was administered, it was determined that there were statistically significant differences in the final course grades of students taking online only credit recovery classes, and blended credit recovery classes using a blended rotational model for both Math I and English I classes. Students taking rotational blended classes had significantly higher means for their final numerical grades as compared to students taking online only classes for both Math I and English I. Future studies should include teacher perceptions of online and blended credit recovery, student motivation using these models, and larger sample sizes comparing different demographics of students