Developing and Comparing Data Mining algorithms that Work Best for Predicting Student’s Performance

Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decision. Identifying potential at-risk students may help instructors and academic guidance to improve the students’ performance and the achievement of learning outcomes. The aim of this research study is to predict at early phases the student’s failure in a particular course using the standards-based grading. Several machines learning techniques were implemented to predict the student failure based on Support Vector Machine, Multilayer Perceptron, Naïve Bayes, and decision tree. The results on each technique shows the ability of machine learning algorithms to predict the student failure accurately after the third week and before the course dropout week. This study provides a strong knowledge for student performance in all courses. It also provides faculty members the ability to help student at-risk by focusing on them and providing necessary support to improve their performance and avoid failure.

Gamified lessons support molecular genetics education of first year biology students during COVID-19 lockdown

This study compared the associated impact of gamified molecular genetics lessons on undergraduate student grades for pre-COVID-19 blended delivery and COVID-19 online only delivery of a first-year biology course. When the molecular genetics gamified lessons were used by on- and off-campus students to support their learning, most students had successful learning outcomes in either blended or online only learning environments. In contrast, students who chose not to use these lessons had significantly greater failure rates for both the molecular biology and the genetics short answer questions in the final invigilated exams. Importantly, there was noticeable gamified lesson fatigue observed by both on- and off-campus students and therefore when incorporating gamified lessons into courses, curriculum design needs to be carefully considered. In conclusion, the use of gamified lessons was associated with significantly reduced student failure rates for molecular genetics concepts studied in a university foundational biology course.

Using Machine Learning for Prediction Students Failure in Morocco: an Application of the CRISP-DM Methodology

Student failure prediction is one of the main topics in university learning contexts, as it helps to avoid failure in higher education institutions and provides a basis to make the teaching and learning process more effective, efficient and reliable. The overall aim of this study is to identify students who are susceptible to fail a given university course. This research paper reports the implementation of an Educational Data Mining project based on the CRISP-DM methodology. The data was collected from the APOGEE system of Ibn Tofail University, a form and specifications of the tested courses. The business goal of this paper is to develop a model that can identify students who are susceptible to failure in a given academic course. Such a model helps prevent failure in higher education institutions and provides a basis for making the teaching and learning process more effective, efficient and reliable. Most common machine learning algorithms in the field of Educational Data Mining were used. The results of our research showed that the proposed method was able to achieve an overall accuracy of 97% in predicting students at potential failure.

School Grade Repetition in Brazil: History of the Configuration of a Political and Educational Problem

This article intends to argue that the movement of students through the Brazilian mandatory school only acquires signs of an educational political problem from the 1930’s on. It indicates that the current sense of the notion of student failure came to be defined only in the twentieth century, although it was possible to fail students since before. It intends to show further that, in articulation with political and cultural changes in education – such as the emergence of compulsory school, the definition of grade-based model of school, and the primacy of homogeneity of classes – the emergence of better and systematic statistics after 1931 contributed decisively in defining the conditions for the possibility of inclusion of student failure as a problem on the political agenda.

ETIOLOGY OF STUDENTS FAIL TO COMPLETE DOCTORATE DEGREE WITHIN STIPULATED TIME

Doctorate degree so-called doctor of philosophy (PhD) is amiably recognized as remarkable postgraduate qualification so far. In the walk of technological advancement and globalization the demand of doctorate degree can’t be abandon and commonly, students with vivid academic background, desire to pursue challenging careers, auspicious personal traits with motivation are enrolled for this programme. When such distinctive students failed to complete the doctorate degree within stipulated time, have arise many questions for stakeholders. This study was conducted to articulate the etiology of student failure to complete doctorate degree programme within stipulated time. For this purpose, PhD students were considered population and data was collected through questionnaire. Total 268 questionnaires were distributed and 233 were received. Statistical tools such as EFA, CFA and SEM were applied. For this SPSS-20 and AMOS-24 software were used. Results of study found Institutional Support, Personality Trait and Supervisor Support have significant positive impact on PhD degree completion. It is recommended that all stakeholders need to play their role and there is dare need to develop a systematic formal organize research structure. Also establish National Research Monitoring Cell for centralization and streamline research activities. Supervisors engage students beyond odds hours also inculcate research / innovation habit.

High School Failure, a Systematic Review in the Social Sciences

Introduction. This study aims to examine the issue of high school failure in social sciences through a systematic review. It aims to provide a critical assessment of research on this subject. It seeks to question the very construct of high school failure, its premises, and the possible consequences from this perspective. Materials and Methods. The research published between 2010–2020, both in Spanish and English in the Scopus and Web of Science databases (Core collection), was considered. A total of 171 articles were identified. After initial screening, 37 papers were finally selected. Semantic maps were created with the Vosviewer. The literature was examined to determine where high school failure is being researched, what type of methodologies are most used and, finally, what impact the research has had on our understanding of this concept. Results. It was found that most of the research on the topic is done in the field of education, and that the methodology used is predominantly quantitative. The different definitions of high school failure tended to attribute its cause to one or more of four reasons: student failure, multicausal phenomena, social exclusion, and finally, disability in the education system. Discussion and Conclusion. It is possible to understand that high school failure is understood and defined as mostly involving studentsʼ responsibility for the academic outcome and achievement obtained. Although studies that cover such factors as a multicausal nature, social exclusion, and the education systemʼs difficulty can be found, the responsibility for failure tends to be attributed to the individual student.

Application of Basic Graph Theory in Autonomous Motion of Robots

Discrete mathematics covers the field of graph theory, which solves various problems in graphs using algorithms, such as coloring graphs. Part of graph theory is focused on algorithms that solve the passage through mazes and labyrinths. This paper presents a study conducted as part of a university course focused on graph theory. The course addressed the problem of high student failure in the mazes and labyrinths chapter. Students’ theoretical knowledge and practical skills in solving algorithms in the maze were low. Therefore, the use of educational robots and their involvement in the teaching of subjects in part focused on mazes and labyrinths. This study shows an easy passage through the individual areas of teaching the science, technology, engineering, and mathematics (STEM) concept. In this article, we describe the research survey and focus on the description and examples of teaching in a university course. Part of the work is the introduction of an easy transition from the theoretical solution of algorithms to their practical implementation on a real autonomous robot. The theoretical part of the course introduced the issues of graph theory and basic algorithms for solving the passage through the labyrinth. The contribution of this study is a change in the approach to teaching graph theory and a greater interconnection of individual areas of STEM to achieve better learning outcomes for science students.

Representing Time Series Data in Intelligent Training Systems

Many of the most popular intelligent training systems, including driving and flight simulators, generate user time series data. This paper presents a comparison of representation options for two different student modeling problems: 1) early failure prediction and 2) classifying student activities. Data for this analysis was gathered from pilots executing simple tasks in a virtual reality flight simulator. We demonstrate that our proposed embedding which uses a combination of dynamic time warping (DTW) and multidimensional scaling (MDS) is valuable for both student modeling tasks. However, Euclidean distance + MDS was found to be a superior embedding for predicting student failure, since DTW can obscure important agility differences between successful and unsuccessful pilots.

Research-Practice Partnership Strategies to Conduct and Use Research to Inform Practice

This study examines the collaborative processes the Chicago Alliance for Equity in Computer Science (CAFÉCS) uses to conduct and use research. The CAFÉCS RPP is a partnership between Chicago Public Schools (CPS), Loyola University Chicago, The Learning Partnership, DePaul University, and University of Illinois at Chicago. Data used in this analysis comes from three years of evaluation data, and includes an analysis of team documents, meeting observations, and interviews with 25 members of the CAFÉCS RPP team. The analysis examines how three problems are being investigated by the partnership: 1) student failure rate in an introductory computer science course, 2) teachers’ limited use of discussion techniques in an introductory computer science class, and 3) computer science teacher retention. Results from the analysis indicate that the RPP engages in a formalized problem-solving cycle. The problem-solving cycle includes the following steps: First, the Office of Computer Science (OCS) identifies a problem. Next, the CAFÉCS team brainstorms and prioritizes hypotheses to test. Next, data analysis clarifies the problem and the research findings are shared and interpreted by the entire team. Finally, the findings are used to inform OCS improvement strategies and next steps for the CAFÉCS research agenda. There are slight variations in the problem-solving cycle, depending on the stage of understanding of the problem, which has implications for the mode of research (e.g hypothesis testing, research and design, continuous improvement, or evaluation).

Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politécnica Nacional

The purpose of this study is to train an artificial neural network model for predicting student failure in the academic leveling course of the Escuela Politécnica Nacional of Ecuador, based on academic and socioeconomic information. For this, 1308 higher education students participated, 69.0% of whom failed the academic leveling course; besides, 93.7% of the students self-identified as mestizo, 83.9% came from the province of Pichincha, and 92.4% belonged to general population. As a first approximation, a neural network model was trained with twelve variables containing students’ academic and socioeconomic information. Then, a dimensionality reduction process was performed from which a new neural network was modeled. This dimension reduced model was trained with the variables application score, vulnerability index, regime, gender, and population segment, which were the five variables that explained more than 80% of the first model. The classification accuracy of the dimension reduced model was 0.745, while precision and recall were 0.883 and 0.778, respectively. The area under ROC curve was 0.791. This model could be used as a guide to lead intervention policies so that the failure rate in the academic leveling course would decrease.