Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course

Engineering dynamics is a fundamental sophomore-level course that is required for nearly all engineering students. As one of the most challenging courses for undergraduates, many students perform poorly or even fail because the dynamics course requires students to have not only solid mathematical skills but also a good understanding of fundamental concepts and principles in the field. A valid model for predicting student academic performance in engineering dynamics is helpful in designing and implementing pedagogical and instructional interventions to enhance teaching and learning in this critical course. The goal of this study was to develop a validated set of mathematical models to predict student academic performance in engineering dynamics. Data were collected from a total of 323 students enrolled in ENGR 2030 Engineering Dynamics at Utah State University for a period of four semesters. Six combinations of predictor variables that represent students’ prior achievement, prior domain knowledge, and learning progression were employed in modeling efforts. The predictor variables include X1 (cumulative GPA), X2~ X5 (three prerequisite courses), X6~ X8 (scores of three dynamics mid-term exams). Four mathematical modeling techniques, including multiple linear regression (MLR), multilayer perceptron (MLP) network, radial basis function (RBF) network, and support vector machine (SVM), were employed to develop 24 predictive models. The average prediction accuracy and the percentage of accurate predictions were employed as two criteria to evaluate and compare the prediction accuracy of the 24 models. The results from this study show that no matter which modeling techniques are used, those using X1 ~X6, X1 ~X7, and X1 ~X8 as predictor variables are always ranked as the top three best-performing models. However, the models using X1 ~X6 as predictor variables are the most useful because they not only yield accurate prediction accuracy, but also leave sufficient time for the instructor to implement educational interventions. The results from this study also show that RBF network models and support vector machine models have better generalizability than MLR models and MLP network models. The implications of the research findings, the limitation of this research, and the future work are discussed at the end of this dissertation

Disadvantaged students' academic performance: analysing the zone of proximal development

The aim of the study is to investigate the practical application of Vygotsky's construct of the Zone of Proximal Development to the selection of disadvantaged students in higher education. There is a need in post-apartheid South Africa, with its legacy of inequality in educational experiences, to find accurate and fair predictors of academic performance that would act as alternatives to matriculation marks and static tests. The study relates the students' response to mediation to their academic performance and analyses the role that non-cognitive factors such as motivation, approaches to learning and learning strategies play in cognitive performance. The investigation was done in the form of different studies using over 400 first year students at the Peninsula Technikon as subjects. The first study focused on the effectiveness of the mediated lessons that form part of the two dynamic tests using a Solomon Four Group and a Two Group design. The second study made a comparison between the predictive validity of past academic achievement conventional static tests, several non-cognitive variables as well as the two dynamic tests. In the third study the students' response to a period of mediation was analysed. The fourth study focused on comparing different groups of students according to the following classification: schooling, gender, language, type of course and assessment and level of course to see whether any of the variables would have a moderator effect Finally a differention was made between the profiles of more successful as opposed to less successful students. The weight of evidence of the study indicates that it is possible to find alternatives to matriculation marks and static tests in selecting disadvantaged students by making use of the concept of the Zone of Proximal Development The results further showed that disadvantaged students are not a homogeneous group. Although the matriculation marks seemed to be the best single predictor of academic performance for the total group of students, alternative predictors were identified when looking at different subgroups. Modifiability (students' response to mediation) had a moderator effect on the predictive power of various variables. For the less modifiable group of students, the matriculation marks and, to a certain extent, static tests were good predictors, while for the more modifiable group of students a dynamic test proved to be a significant predictor of academic performance. The implications of the findings for the selection and academic development of disadvantaged students are discussed

A Predictive Model using Machine Learning Algorithm in Identifying Student's Probability on Passing Semestral Course

Purpose: The used of an integrated academic information system in higher education has been proven in improving quality education which results to generates enormous data that can be used to discover new knowledge through data mining concepts, techniques, and machine learning algorithm. This study aims to determine a predictive model to learn students' probability to pass their courses taken at the earliest stage of the semester. Method: To successfully discover a good predictive model with high acceptability, accurate, and precision rate which delivers a useful outcome for decision making in education systems, in improving the processes of conveying knowledge and uplifting student's academic performance, the proponent applies and strictly followed the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. This study employs classification for data mining techniques, and decision tree for algorithm. Results: With the utilization of the newly discovered predictive model, the prediction of students' probabilities to pass the current courses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and 0.8571 f1 score, which shows that the model used in the prediction is reliable, accurate, and recommendable. Conclusion: Considering the indicators and the results, it can be noted that the prediction model used in this study is highly acceptable. The data mining techniques provides effective and efficient innovative tools in analyzing and predicting student performances. The model used in this study will greatly affect the way educators understand and identify the weakness of their students in the class, the way they improved the effectiveness of their learning processes gearing to their students, bring down academic failure rates, and help institution administrators modify their learning system outcomes. Recommendations: Full automation of prediction results accessible by the students, faculty, and institution administrators for fast management decision making should take place. Further study for the inclusion of some student`s demographic information, vast amount of data within the dataset, automated and manual process of predictive criteria indicators where the students can regulate to which criteria, they must improve more for them to pass their courses taken at the end of the semester as early as midterm period are highly needed

Rethinking Metacognitive Intervention: A Scaffolded Exam Wrapper Strategy

Students lack the behaviors and strategies that support success in postsecondary environments, which has led one-third of all college students to enroll in remedial courses (Bowen, Chingos, & McPherson, 2009). One particular executive function that low-achieving students are often without is metacognition, or thinking about thinking. Traditional models of education in the United States do not teach students how to analyze their performance even though metacognition is linked to improved academic performance (Young & Fry, 2008). This work presents a scaffolded metacognitive strategy to help low-achieving students improve their metacognitive skillfulness and examination performance

Peer Led Team Learning in Introductory Biology: Effects on Critical Thinking Skills

This study evaluated the potential effects of the Peer-Led Team Learning (PLTL) instructional model on undergraduate, biology peer leaders\u27 critical thinking skills. This investigation also explored peer leaders\u27 perceptions of their critical thinking skills. A quasi-experimental pre-test/post-test with control group design was used to determine critical thinking gains in PLTL/non-PLTL groups. Critical thinking was assessed using the California Critical Thinking Skills Test (CCTST) among participants who had previously completed and been successful in the second semester of a two-semester introductory biology course sequence. Qualitative data from open-ended questionnaires confirmed that factors thought to improve critical thinking skills such as interaction with peers, problem solving, and discussion were perceived by participants to have an impact on critical thinking gains. However, no significant quantitative differences in peer leaders\u27 critical thinking skills were found between pre- and post-treatment CCTST measurements nor between experimental and control groups. Additionally, students led by peer leaders attained significantly higher exam and final course grades in introductory biology than similar students not participating in PLTL. Finally, among introductory biology students who opted not to enroll in the associated lab course, those who participated in PLTL averaged more than a letter grade higher than those who did not, and this difference was statistically significant

Locus of Control & Motivation Strategies for Learning Questionnaire: Predictors of Student Success on the ATI Comprehensive Predictor Exam & NCLEX-RN Examination

ABSTRACT The two purposes of this study were to determine whether locus of control (LOC) was predictive of how a student would perform on the ATI Comprehensive Predictor Exam and the NCLEX-RN, and if the Motivated Strategies for Learning Questionnaire (MSLQ) provided information that would help determine predictors of success on these two exams. The study examined additional variables prominent in the literature including but not limited to, the number of Cs a person earned while in nursing school, and grades in courses such as pharmacology, pathophysiology, and medical/surgical nursing. The influence of a job was also investigated. It was believed that an individual with an internal locus of control (LOC) would be more likely to be successful on the ATI Comprehensive Predictor Exam and the NCLEX-RN. Internal LOC was found to be statistically significant related to the NCLEX-RN. Using logistic regression a student with an internal LOC when entered into the model with the ATI Comprehensive Predictor Exam was 6.7 times more likely to pass the NCLEX-RN. Using regression analysis this was not found to be true in relationship to the ATI Comprehensive Predictor. The model that was the best predictor of a student's success on the ATI exam included the MSLQ subscales of Test Anxiety, Organization, Self-Regulation, Pharmacology course, the first Medical/Surgical class, Job not healthcare related, and the ATI Medical/Surgical Content Mastery Exam. These seven variables were the best at predicting success. A sub-hypothesis related to student performance on the ATI Medical/Surgical Content Mastery Exam believed that a student with an internal LOC would be more successful, this did not prove to be true. The students with an external LOC had pass rate of 50% on the exam at a Level two proficiency compared to 45.28% passing with an internal LOC. The number of students in the sample that were determined to have an external LOC was very small (n=12) while the results in this study were not statistically significant it is possible that a sample with a larger sample of students with an external LOC may produce different results. An additional finding was a student working in a healthcare related job or not working scored 2.278 points higher on the ATI Comprehensive Predictor Exam than those working in a non-healthcare related job. The second hypothesis examined the MSLQ subscales that were predictive of success on the two exams. In terms of the ATI Comprehensive Predictor Exam the subscales that entered into the model were test anxiety, organization, and self-regulation. When determining the MSLQ subscales that were important related to success on the NLCEX, control of learning beliefs and organization were the only two subscales in the model. Those subscales statistically significant in terms of a student achieving Level 2 proficiency on the ATI Medical/Surgical Content Mastery Exam were test anxiety, rehearsal, organization, and peer learning. When evaluating test anxiety it was determined that as the MSLQ test anxiety score increased for the individual, the odds of passing decreased. Of the individuals with a test anxiety subscale score of 2.9 (scale of 1-7) or less all were successful on the NCLEX-RN. Results indicated that of those students with a test anxiety subscale score of 5.0 or higher, ten students failed the ATI Comprehensive Predictor Exam and four students failed the NCLEX-RN. An additional hypothesis stated that a student's results on the ATI Medical/Surgical Content Mastery Exam would be predictive of his or her performance on the ATI Comprehensive Predictor Exam. This hypothesis was found to be true. A student scoring at Level II proficiency (mastery of content per ATI Faculty Resource Guide, 2007) was likely to score 4.391 points higher than a student at Level 1 proficiency. As the level of proficiency increased so did the percentage of passing the NCLEX-RN. A student who scored below level one had a 58.33% pass rate on NCLEX-RN compared to a level two proficiency pass rate of 92.68%. When looking at student grades in the first medical/surgical course only 70.59% of the students obtaining the letter grade of C passed the NCLEX-RN. The percentage improved with the second medical/surgical course, 80.77% of students with a C passed. Of those students earning a C in pharmacology only 75% of the students passed the NCLEX-RN

Measuring academic performance of students in Higher Education using data mining techniques

Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems. It aims to use those methods to achieve a logical understanding of students, and the educational environment they should have for better learning. These data are characterized by their large size and randomness and this can make it difficult for educators to extract knowledge from these data. Additionally, knowledge extracted from data by means of counting the occurrence of certain events is not always reliable, since the counting process sometimes does not take into consideration other factors and parameters that could affect the extracted knowledge. Student attendance in Higher Education has always been dealt with in a classical way, i.e. educators rely on counting the occurrence of attendance or absence building their knowledge about students as well as modules based on this count. This method is neither credible nor does it necessarily provide a real indication of a student s performance. On other hand, the choice of an effective student assessment method is an issue of interest in Higher Education. Various studies (Romero, et al., 2010) have shown that students tend to get higher marks when assessed through coursework-based assessment methods - which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of Educational Data Mining (EDM) studies that pre-processed data through the conventional Data Mining processes including the data preparation process, but they are using transcript data as it stands without looking at examination and coursework results weighting which could affect prediction accuracy. This thesis explores the above problems and tries to formulate the extracted knowledge in a way that guarantees achieving accurate and credible results. Student attendance data, gathered from the educational system, were first cleaned in order to remove any randomness and noise, then various attributes were studied so as to highlight the most significant ones that affect the real attendance of students. The next step was to derive an equation that measures the Student Attendance s Credibility (SAC) considering the attributes chosen in the previous step. The reliability of the newly developed measure was then evaluated in order to examine its consistency. In term of transcripts data, this thesis proposes a different data preparation process through investigating more than 230,000 student records in order to prepare students marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students module marks are refined during the data preparation process. The results of this work show that students final marks should not be isolated from the nature of the enrolled module s assessment methods; rather they must be investigated thoroughly and considered during EDM s data pre-processing phases. More generally, it is concluded that Educational Data should not be prepared in the same way as exist data due to the differences such as sources of data, applications, and types of errors in them. Therefore, an attribute, Coursework Assessment Ratio (CAR), is proposed to use in order to take the different modules assessment methods into account while preparing student transcript data. The effect of CAR and SAC on prediction process using data mining classification techniques such as Random Forest, Artificial Neural Networks and k-Nears Neighbors have been investigated. The results were generated by applying the DM techniques on our data set and evaluated by measuring the statistical differences between Classification Accuracy (CA) and Root Mean Square Error (RMSE) of all models. Comprehensive evaluation has been carried out for all results in the experiments to compare all DM techniques results, and it has been found that Random forest (RF) has the highest CA and lowest RMSE. The importance of SAC and CAR in increasing the prediction accuracy has been proved in Chapter 5. Finally, the results have been compared with previous studies that predicted students final marks, based on students marks at earlier stages of their study. The comparisons have taken into consideration similar data and attributes, whilst first excluding average CAR and SAC and secondly by including them, and then measuring the prediction accuracy between both. The aim of this comparison is to ensure that the new preparation process stage will positively affect the final results