Predicting Pre-university Student's Mathematics Achievement

AbstractThis study exploits three methods, namely the Back-propagation Neural Network (BPNN), Classification and Regression Tree (CART), and Generalized Regression Neural Network (GRNN) in predicting the student's mathematics achievement. The first part of this study utilizes enrolment data to predict the student's mid-semester evaluation result, whereas the latter part employs additional data to predict the student's final examination result. The predictive model's accuracy is evaluated using 10-fold cross-validation to identify the best model. The findings reveal that BPNN outperforms other models with an accuracy of 66.67% and 71.11% in predicting the mid-semester evaluation result and the final examination result respectively

Neural network methods for one-to-many multi-valued mapping problems

An investigation of the applicability of neural network-based methods in predicting the values of multiple parameters, given the value of a single parameter within a particular problem domain is presented. In this context, the input parameter may be an important source of variation that is related with a complex mapping function to the remaining sources of variation within a multivariate distribution. The definition of the relationship between the variables of a multivariate distribution and a single source of variation allows the estimation of the values of multiple variables given the value of the single variable, addressing in that way an ill-conditioned one-to-many mapping problem. As part of our investigation, two problem domains are considered: predicting the values of individual stock shares, given the value of the general index, and predicting the grades received by high school pupils, given the grade for a single course or the average grade. With our work, the performance of standard neural network-based methods and in particular multilayer perceptrons (MLPs), radial basis functions (RBFs), mixture density networks (MDNs) and a latent variable method, the general topographic mapping (GTM), is compared. According to the results, MLPs and RBFs outperform MDNs and the GTM for these one-to-many mapping problems

PREDICTING STUDENTS«¤?? GRADE SCORES USING TRAINING FUNCTIONS OF ARTIFICIAL NEURAL NETWORK

The observed poor quality of graduates of some Nigerian Universities in recent times has been traced to non-availability of adequate mechanism. This mechanism is expected to assist the policy maker project into the future performance of students, in order to discover at the early stage, students who have no tendency of doing well in school. This study focuses on the use of artificial neural network (ANN) model for predicting students«¤?? academic performance in a University System, based on the previous datasets. The domain used in the study consists of sixty (60) students in the Department of Computer and Information Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. The codes were written and executed using MATLAB format. The students«¤?? CGPA from first year through their third year were used as the inputs to train the ANN models constructed using nntool and the Final Grades (CGPA) served as a target output. The output predicted by the networks is expressed in-line with the current grading system of the case study. CGPA values simulated by the network are compared with the actual final CGPA to determine the efficacy of each of the three feed-forward neural networks used. Test data evaluations showed that the ANN model is able to predict correctly, the final grade of students with 91.7% accuracy.ª¤

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

View recommendation for multi-camera demonstration-based training

While humans can effortlessly pick a view from multiple streams, automatically choosing the best view is a challenge. Choosing the best view from multi-camera streams poses a problem regarding which objective metrics should be considered. Existing works on view selection lack consensus about which metrics should be considered to select the best view. The literature on view selection describes diverse possible metrics. And strategies such as information-theoretic, instructional design, or aesthetics-motivated fail to incorporate all approaches. In this work, we postulate a strategy incorporating information-theoretic and instructional design-based objective metrics to select the best view from a set of views. Traditionally, information-theoretic measures have been used to find the goodness of a view, such as in 3D rendering. We adapted a similar measure known as the viewpoint entropy for real-world 2D images. Additionally, we incorporated similarity penalization to get a more accurate measure of the entropy of a view, which is one of the metrics for the best view selection. Since the choice of the best view is domain-dependent, we chose demonstration-based training scenarios as our use case. The limitation of our chosen scenarios is that they do not include collaborative training and solely feature a single trainer. To incorporate instructional design considerations, we included the trainer’s body pose, face, face when instructing, and hands visibility as metrics. To incorporate domain knowledge we included predetermined regions’ visibility as another metric. All of those metrics are taken into account to produce a parameterized view recommendation approach for demonstration-based training. An online study using recorded multi-camera video streams from a simulation environment was used to validate those metrics. Furthermore, the responses from the online study were used to optimize the view recommendation performance with a normalized discounted cumulative gain (NDCG) value of 0.912, which shows good performance with respect to matching user choices

THE CONTRIBUTIONS OF CRYSTALLIZED CROSS-DOMAIN KNOWLEDGE AND FLUID RELATIONAL REASONING ABILITY TO NINTH- AND TWELFTH-GRADE STUDENTS’ PERFORMANCE ON SCHOLASTIC APTITUDE AND CONTENT-SPECIFIC ACHIEVEMENT MEASURES

This study investigated how measures of both crystallized cross-domain knowledge and fluid relational reasoning abilities contribute to high-school students’ scholastic aptitude and content-specific achievement. The participants for this study were 211 ninth-grade and 76 twelfth-grade students enrolled in an all-male parochial high school. A series of multivariate multiple linear regression tests were conducted to examine the ability of the three crystallized cross-domain knowledge predictor variables (i.e., Language Skills, Mathematics, Reading Comprehension) and fluid reasoning ability predictor variable (Test of Relational Reasoning - TORR) to predict performance of ninth-grade students’ scholastic aptitude (Preliminary Scholastic Assessment Test - PSAT), and content-specific achievement (Social Studies, Algebra, and Spanish final examinations); and performance of twelfth-grade students’ content-specific achievement (Advanced Placement Psychology examination). Results of these analyses revealed that fluid relational reasoning was the strongest unique predictor of performance for ninth-grade students on the PSAT, Algebra and Spanish final examinations, and for twelfth-grade students on the AP Psychology examination. Crystallized cross-domain was found to be not as strong of a predictor as fluid relational reasoning on the five outcome measures. Results from this study suggest that students who have greater fluid relational reasoning abilities may perform better on these assessments. The research also includes delimitations, practical limitations for educators, and suggestions for future research to expand the scope of this study

Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn

The purpose of this study was to discover factors about first-time freshmen that began at one of the six 4-year universities in the former Tennessee Board of Regents (TBR) system, transferred to any other institution after their first year, and graduated with a degree or certificate. These factors would be used with predictive models to identify these students prior to their initial departure. Thirty-four variables about students and the institutions that they attended and graduated from were used to perform principal component analysis to examine the factors involved in their decisions. A subset of 18 variables about these students in their first semester were used to perform principal component analysis and produce a set of 4 factors that were used in 5 predictive models. The 4 factors of students who transferred and graduated elsewhere were “Institutional Characteristics,” “Institution’s Focus on Academics,” “Student Aptitude,” and “Student Community.” These 4 factors were combined with the additional demographic variables of gender, race, residency, and initial institution to form a final dataset used in predictive modeling. The predictive models used were a logistic regression, decision tree, random forest, artificial neural network, and support vector machine. All models had predictive power beyond that of random chance. The logistic regression and support vector machine models had the most predictive power, followed by the artificial neural network, random forest, and decision tree models respectively

Bayesian Networks with Expert Elicitation as Applicable to Student Retention in Institutional Research

The application of Bayesian networks within the field of institutional research is explored through the development of a Bayesian network used to predict first- to second-year retention of undergraduates. A hybrid approach to model development is employed, in which formal elicitation of subject-matter expertise is combined with machine learning in designing model structure and specification of model parameters. Subject-matter experts include two academic advisors at a small, private liberal arts college in the southeast, and the data used in machine learning include six years of historical student-related information (i.e., demographic, admissions, academic, and financial) on 1,438 first-year students. Netica 5.12, a software package designed for constructing Bayesian networks, is used for building and validating the model. Evaluation of the resulting model’s predictive capabilities is examined, as well as analyses of sensitivity, internal validity, and model complexity. Additionally, the utility of using Bayesian networks within institutional research and higher education is discussed. The importance of comprehensive evaluation is highlighted, due to the study’s inclusion of an unbalanced data set. Best practices and experiences with expert elicitation are also noted, including recommendations for use of formal elicitation frameworks and careful consideration of operating definitions. Academic preparation and financial need risk profile are identified as key variables related to retention, and the need for enhanced data collection surrounding such variables is also revealed. For example, the experts emphasize study skills as an important predictor of retention while noting the absence of collection of quantitative data related to measuring students’ study skills. Finally, the importance and value of the model development process is stressed, as stakeholders are required to articulate, define, discuss, and evaluate model components, assumptions, and results