The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

Examination of Potential Factors to Predict Fieldwork Performance: A Program Evaluation Project

This program evaluation project evaluated the validity of a hypothesized model for predicting fieldwork performance using data of 121 occupational therapy students from a single university. The first aim was to evaluate the hypothesized relationships between observed measures (e.g., admission GPAs) and proposed latent factors (e.g., academic achievement) for predictor and outcome variables. Factor analysis of the outcome variable revealed a three-factor structure, measured by 13 items from the Fieldwork Performance Evaluation for the Occupational Therapy Student. However, factor analyses of the predictor variables did not support the proposed latent factors: Academic Achievement and Professional Potential. The second aim was to evaluate the hypothesized effects of predictor variables on level II fieldwork performance. Results of the structural equation modeling (SEM) analysis supported some of the hypothesized relationships. The model was a good fit to the data; however, the final SEM model only accounted for 16.4% of the variance. Results showed that four of the eight observed variables were predictive. Two academic measures (i.e., admission overall GPA and science GPA) and two non-academic measures (i.e., Myer’s Briggs Thinking type indicator and number of observation hours) demonstrated small predictive relationships with Evaluation Skills. Admission overall GPA and thinking type indicator had positive predictive relationships; whereas, admission science GPA and number of hours had inverse relationships. None of the observed variables predicted the other two fieldwork performance factors: Professional Behaviors and Intervention Skills. Although the results of this project did not fully support the hypothesized model, some interesting findings emerged for future exploration

Exploring Support Seeking Behaviours of First-Year Students to Predict Academic Performance

Academic performance during first year is critical in determining student retention rates, later undergraduate performance, and career related prospects. Previous literature has assessed importance of predictors individually. This study combined predictors to develop a model to predict academic performance of first-year students (n = 90) based on motivated learning strategies and on-campus resource use. An online survey was created to evaluate students’ help-seeking (HS), peer learning (PL), self-efficacy (SE), perceived social support (PSS) and access to social support (SSA) and academic support (ASA) resources. Consistent with previous research, SE was the strongest predictor of academic performance. Additionally, HS, SE, ASA, and SSA combined contributed to a significant model accounting for 37% of variance in students’ academic performance. The results observed low levels of resource access. These results contribute to the furthering of predictive modeling algorithms, improving access to resource use on-campus, and enhancing academic performance of first-year students during the adjustment to university life

Dropout Model Evaluation in MOOCs

The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply this method to a series of algorithms and feature sets derived from a large sample of Massive Open Online Courses (MOOCs). While a complete comparison of all potential modeling approaches is beyond the scope of this paper, we show that this approach reveals a large gap in dropout prediction performance between forum-, assignment-, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another. This work has methodological implications for evaluating predictive or AI-based models of student success, and practical implications for the design and targeting of at-risk student models and interventions

Cognitive Aptitude as a Predictor of Success In Associate Degree Nursing Programs

Student success in nursing education is essential to supplement the healthcare workforce and sustain the delivery of safe and efficient nursing care. However, the loss of students who drop out or fail out of nursing programs is alarmingly high even though institutions have sought to identify the best candidates for admission to rigorous nursing curricula. While most nursing programs have used academic measures, such as grade point average or standardized testing to rank students for admission, these measures have not adequately captured the characteristics that students must possess to be successful. To further identify nonacademic attributes that enhance achievement, new criteria are being explored. This study tests a new model, the Nursing Cognitive Aptitude Model, or NCAM (Twidwell et al., 2018) as an organizational framework to examine the variables of prior academic performance, current knowledge, and critical thinking skills, for its ability to predict early student success in an associate degree nursing program. A convenience sample of 115 first semester nursing students completed two instruments, the Health Sciences Reasoning Test, and the Test of Essential Academic Skills. Student scores as well as both pre-nursing and nursing cumulative grade point averages were evaluated using regression analysis. The results were consistent with existing evidence that prior academic performance and current knowledge, as measured by composite scores on standardized testing, were significantly related to student performance. However, overall critical thinking skill did not contribute to early success in nursing education. Thus, the combined composite scores of each variable included in the NCAM did not significantly predict nursing grade point average. Additional inquiry with multisite designs and diverse student populations is needed to understand the role of pre-existing critical thinking skills in the educational process and to further evaluate the NCAM as a predictive model for student success

Assessing Experiential Learning in Construction Education by Modeling Student Performance

abstract: The typical engineering curriculum has become less effective in training construction professionals because of the evolving construction industry needs. The latest National Science Foundation and the National Academies report indicate that industry-valued skills are changing. The Associated General Contractors of America recently stated that contractors expect growth in all sectors; however, companies are worried about the supply of skilled professionals. Workforce development has been of a growing interest in the construction industry, and this study approaches it by conducting an exploratory analysis applied to students that have completed a mandatory internship as part of their construction program at Arizona State University, in the School of Sustainable Engineering and the Built Environment. Data is collected from surveys, including grades by a direct evaluator from the company reflecting each student’s performance based on recent Student Learning Objectives. Preliminary correlations are computed between scores received on the 15 metrics in the survey and the final industry suggested grade. Based on the factors identified as highest predictors: ingenuity and creativity, punctuality and attendance, and initiative; a prognostic model of student performance in the construction industry is generated. With regard to graduate employability, student performance in the industry and human predispositions are also tested in order to evaluate their contribution to the generated model. The study finally identifies threats to validity and opportunities presented in a dynamic learning environment presented by internships. Results indicate that measuring student performance during internships in the construction industry creates challenges for the evaluator from the host company. Scoring definitions are introduced to standardize the evaluators’ grading based on observations of student behavior. 12 questions covering more Student Learning Objectives identified by the industry are added to the survey, potentially improving the reliability of the predictive model.Dissertation/ThesisDoctoral Dissertation Construction Management 201