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

Using Connectionist Models to Evaluate Examinees’ Response Patterns to Achievement Tests

The attribute hierarchy method (AHM) applied to assessment engineering is described. It is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive test specifications. These specifications identify the examinees’ attribute patterns which are used as output for the neural network. The purpose of the statistical pattern recognition analysis is to estimate the probability that an examinee possess specific attribute combinations based on their observed item response patterns. Two examples using student response data from a sample of algebra items on the SAT illustrate our pattern recognition approach

Development of an intelligent approach for delivering high performing training solutions

Predictive modelling is a state-of-the-art technique for which an objective variable is predicted given a set of environmental parameters. Many types of models exist and most of them can be built with different architectures. This project comes from the necessity to improve the measurement of biometric indicators and understand how team performance can affect their level in a defence training context. The biometric indicators of interest are the player’s heart rate and stress index, which are going to be related to information extracted from the GPS coordinates they have. The objective has been to study the evolution of the stress index during an exercise period, trying to determine how the other biometrical and positional factors influence its level. After reviewing existing work in the medical field for stress monitoring and team performance in an educational context, it has been observed that no literature involved both topics relating them to each other, let alone in a defence environment. A list of quality indicators has been defined to assess the quality of the provided raw datasets, the information from which has been managed to build a single dataset that could be used to train a model. The results of the quality assessment have shown that the recording frequency for the different indicators should be modified to a common value since the existing time difference between recordings has proven to be a complex issue to solve when building the model. Regression, neural network, and random forest models have been tested, with the latter being the one offering the best precision. The heart rate, the duration of the exercise, and the distance from the player to the opposite team were the variables that played a major role in the prediction. Overall, a valid prediction has been reached despite the missing gaps in the provided datasets. The key features to predict the stress index have been identified and recommendations in terms of data quality have been made so the predictions can be improve

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

oai:flr.journals.publicknowledgeproject.org:article/13Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students’ outcomes. Some authors have suggested that traditional statistical methods do not always yield accurate predictions and/or classifications (Everson, 1995; Garson, 1998). This paper explores a relatively new methodological approach for the field of learning and education, but which is widely used in other areas, such as computational sciences, engineering and economics. This study uses cognitive and non-cognitive measures of students, together with background information, in order to design predictive models of student performance using artificial neural networks (ANN). These predictions of performance constitute a true predictive classification of academic performance over time, a year in advance of the actual observed measure of academic performance. A total sample of 864 university students of both genders, ages ranging between 18 and 25 was used. Three neural network models were developed. Two of the models (identifying the top 33% and the lowest 33% groups, respectively) were able to reach 100% correct identification of all students in each of the two groups. The third model (identifying low, mid and high performance levels) reached precisions from 87% to 100% for the three groups. Analyses also explored the predicted outcomes at an individual level, and their correlations with the observed results, as a continuous variable for the whole group of students. Results demonstrate the greater accuracy of the ANN compared to traditional methods such as discriminant analyses.  In addition, the ANN provided information on those predictors that best explained the different levels of expected performance. Thus, results have allowed the identification of the specific influence of each pattern of variables on different levels of academic performance, providing a better understanding of the variables with the greatest impact on individual learning processes, and of those factors that best explain these processes for different academic levels

Use of deep multi-target prediction to identify learning styles

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students105Applied machine learnin

Classification of EEG signals of user states in gaming using machine learning

In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and k-nearest neighbor classify the three user states -- Boredom, Flow, and Anxiety? (ii) Can we distinguish the flow state from other user states using machine learning models? (iii) What are the essential components of EEG signals for classifying the three user states? To extract the critical components of EEG signals, a feature selection method known as minimum redundancy and maximum relevance method was implemented. An average accuracy of 85 % is achieved for classifying the three user states by using the support vector machine classifier --Abstract, page iii

Constructed response or multiple-choice questions for assessing declarative programming knowledge? That is the question!

Aim/Purpose This paper presents a data mining approach for analyzing responses to advanced declarative programming questions. The goal of this research is to find a model that can explain the results obtained by students when they perform exams with Constructed Response questions and with equivalent Multiple-Choice Questions. Background The assessment of acquired knowledge is a fundamental role in the teachinglearning process. It helps to identify the factors that can contribute to the teacher in the developing of pedagogical methods and evaluation tools and it also contributes to the self-regulation process of learning. However, better format of questions to assess declarative programming knowledge is still a subject of ongoing debate. While some research advocates the use of constructed responses, others emphasize the potential of multiple-choice questions. Methodology A sensitivity analysis was applied to extract useful knowledge from the relevance of the characteristics (i.e., the input variables) used for the data mining process to compute the score. Contribution Such knowledge helps the teachers to decide which format they must consider with respect to the objectives and expected students results. Findings The results shown a set of factors that influence the discrepancy between answers in both formats. Recommendationsfor Practitioners Teachers can make an informed decision about whether to choose multiplechoice questions or constructed-response taking into account the results of this study. Recommendations for Researchers In this study a block of exams with CR questions is verified to complement the area of learning, returning greater performance in the evaluation of students and improving the teaching-learning process. Impact on Society The results of this research confirm the findings of several other researchers that the use of ICT and the application of MCQ is an added value in the evaluation process. In most cases the student is more likely to succeed with MCQ, however if the teacher prefers to evaluate with CR other research approaches are needed. Future Research Future research must include other question formats.info:eu-repo/semantics/publishedVersio

Modeling the contribution of genetic variation to cognitive gains following training with a machine learning approach

The objective of this research was to develop robust predictive models of the gains in working memory (WM) and fluid intelligence (Gf) following executive attention training in children, using genetic markers, gender, and age variables. We explore the influence of genetic variables on individual differences in susceptibility to intervention. Sixty-six children (males: 54.2%) aged 50.9-75.9 months participated in a four-weeks computerized training program. Information on genes involved in the regulation of dopamine, serotonin, norepinephrine, and acetylcholine was collected. The standardized pre- to post-training gains of two dependent measures were considered: WM Span backwards condition (WISC-III) and the IQ-f factor from the Kaufman Brief Intelligence Test (K-BIT). A machine-learning methodology was implemented utilizing multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm. Both ANN models reached high overall accuracy in their predictive classification. Variations in genes involved in dopamine and norepinephrine neurotransmission affect children's susceptibility to benefit from executive attention training, a pattern that is consistent with previous studies

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities

Towards Student Engagement Analytics: Applying Machine Learning to Student Posts in Online Lecture Videos

The use of online learning environments in higher education is becoming ever more prevalent with the inception of MOOCs (Massive Open Online Courses) and the increase in online and flipped courses at universities. Although the online systems used to deliver course content make education more accessible, students often express frustration with the lack of assistance during online lecture videos. Instructors express concern that students are not engaging with the course material in online environments, and rely on affordances within these systems to figure out what students are doing. With many online learning environments storing log data about students usage of these systems, research into learning analytics, the measurement, collection, analysis, and reporting data about learning and their contexts, can help inform instructors about student learning in the online context. This thesis aims to lay the groundwork for learning analytics that provide instructors high-level student engagement data in online learning environments. Recent research has shown that instructors using these systems are concerned about their lack of awareness about student engagement, and educational psychology has shown that engagement is necessary for student success. Specifically, this thesis explores the feasibility of applying machine learning to categorize student posts by their level of engagement. These engagement categories are derived from the ICAP framework, which categorizes overt student behaviors into four tiers of engagement: Interactive, Constructive, Active, and Passive. Contributions include showing what natural language features are most indicative of engagement, exploring whether this machine learning method can be generalized to many courses, and using previous research to develop mockups of what analytics using data from this machine learning method might look like