scholarly journals USING EDUCATIONAL DATA MINING IN ASSESSMENT OF STUDENT ACHIEVEMENT

2020 ◽  
Vol 8 (48) ◽  
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Alternative Assessment ◽  
Peer Assessment ◽  
Student Assessment ◽  
Educational Data Mining ◽  
Large Data ◽  
Educational Process ◽  
Assessment Instruments ◽  
Assessment And Evaluation

Education is the deliberate enculturation process in general. The concept of deliberation here emphasizes a program that does not leave expectations to coincidences and thus excludes unwanted situations. No matter how accurately and effectively this program is organized, quality control is still carried out at the end of the process with assessment and evaluation processes. The assessment and evaluation processes in education provide feedback in terms of the effectiveness of both the student and the program. This would also lead to an effective reorganization of the process. One of the problems faced during the transition from product or outcome based student assessment approaches to process-based alternative assessment approaches is the difficulty in evaluating the student data collected by more than one alternative assessment instruments. Using all the data about the student in determining the academic achievement of students affects the success of process assessment approach positively. Educational Data Mining is the computer aided search of the relations and rules that enable us to make predictions about the present and the future through the use of the massive amount of data concerning the educational process obtained from various sources. With this process, patterns, similarities and correlations that are in a large data warehouse can be determined and interpreted by using any of pattern recognition methods. Through enabling holistic evaluation of data obtained by process evaluation oriented assessment instruments such as portfolio, rubrics, self and peer assessment, performance assessment etc. it will be possible to obtain the relations concerning not only students’ academic achievement but also students, teachers, schools and courses. Keywords: Assessment, alternative assessment, data mining, educational data mining

scholarly journals Peer Assessment in Writing Expository Essays: A Research on Tenth Graders of Vocational School

2021 ◽  
Vol 20 ◽  
pp. 134-142
Author(s):  
Supriyadi Supriyadi
Keyword(s):  
Learning Strategies ◽  
Teaching And Learning ◽  
Peer Assessment ◽  
Student Assessment ◽  
Practical Experience ◽  
Average Score ◽  
Assessment Instruments ◽  
Tenth Graders ◽  
The Impact ◽  
Assessment Activities

The present work was devoted to: (a) exploring peer assessment instruments (i.e., assessment sheets) utilized by teachers in teaching expository essay writing for tenth graders of vocational schools; (b) discussing the implementation and (c) the impact of peer assessment of the peer assessment activities. In this study, the data were from information in the peer assessment implementation.  Methods of data collection involved observation, interview, and documentation. The results showed that: (a) The peer assessment instruments were in compliance with the competence standards in student assessment. (b) The implementation of expository writing teaching and learning was to find out students’ progress and outcomes; it also aimed to improve learning processes. (c) Peer assessment activities help schools enhance their learning quality and teachers in comprehending the peer assessment processes. On top of that, the assessment provides students with practical experience in assessing their peers’ works, thus boosting students’ concentration in learning. Peer assessment also measures the affective domains of students (by which it eases teachers to select appropriate teaching-learning strategies). In conclusion, the assessment on students’ social attitude fall under a good category based on the students’ average score.

scholarly journals Formulating Module Assessment for Improved Academic Performance Predictability in Higher Education

2019 ◽  
Vol 9 (3) ◽  
pp. 4287-4291 ◽  
Author(s):  
M. Alsuwaiket ◽  
A. H. Blasi ◽  
R. A. Al-Msie'deen
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Student Assessment ◽  
Educational Data Mining ◽  
Assessment Method ◽  
Assessment Methods ◽  
Preparation Process ◽  
Data Preparation ◽  
Data Types ◽  
Process Data

The choice of an effective student assessment method is an issue of interest in Higher Education. Various studies [1] 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-process data through conventional data mining processes including data preparation process, but they are using transcript data as they stand without looking at examination and coursework results weighting which could affect prediction accuracy. This paper 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. They must rather 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 other data types due to differences as data sources, applications, and types of errors in them. Therefore, an attribute, coursework assessment ratio (CAR), is proposed to be used in order to take the different modules’ assessment methods into account while preparing student transcript data. The effect of CAR on prediction process using the random forest classification technique has been investigated. It is shown that considering CAR as an attribute increases the accuracy of predicting students’ second-year averages based on their first-year results.

scholarly journals Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

2019 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Cut Fiarni ◽  
Evasaria M. Sipayung ◽  
Prischilia B.T. Tumundo
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Educational Data Mining ◽  
Educational Process ◽  
Student Interest ◽  
The Impact

Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order  to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.Methods: We implemented factor analysis method and decision tree method  of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information

scholarly journals Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions

10.28945/4835 ◽  
2021 ◽  
Vol 20 ◽  
pp. 121-137
Author(s):  
Sarah Alturki ◽  
Nazik Alturki ◽  
Heiner Stuckenschmidt
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Naive Bayes ◽  
Language Skills ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
Future Research ◽  
Core Courses ◽  
Students Success

Aim/Purpose: One of the main objectives of higher education institutions is to provide a high-quality education to their students and reduce dropout rates. This can be achieved by predicting students’ academic achievement early using Educational Data Mining (EDM). This study aims to predict students’ final grades and identify honorary students at an early stage. Background: EDM research has emerged as an exciting research area, which can unfold valuable knowledge from educational databases for many purposes, such as identifying the dropouts and students who need special attention and discovering honorary students for allocating scholarships. Methodology: In this work, we have collected 300 undergraduate students’ records from three departments of a Computer and Information Science College at a university located in Saudi Arabia. We compared the performance of six data mining methods in predicting academic achievement. Those methods are C4.5, Simple CART, LADTree, Naïve Bayes, Bayes Net with ADTree, and Random Forest. Contribution: We tested the significance of correlation attribute predictors using four different methods. We found 9 out of 18 proposed features with a significant correlation for predicting students’ academic achievement after their 4th semester. Those features are student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses, i.e., database fundamentals, programming language (1), and computer network fundamentals. Findings: The empirical results show the following: (i) the main features that can predict students’ academic achievement are the student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses; (ii) Naïve Bayes classifier performed better than Tree-based Models in predicting students’ academic achievement in general, however, Random Forest outperformed Naïve Bayes in predicting honorary students; (iii) English language skills do not play an essential role in students’ success at the college of Computer and Information Sciences; and (iv) studying an orientation year does not contribute to students’ success. Recommendations for Practitioners: We would recommend instructors to consider using EDM in predicting students’ academic achievement and benefit from that in customizing students’ learning experience based on their different needs. Recommendation for Researchers: We would highly endorse that researchers apply more EDM studies across various universities and compare between them. For example, future research could investigate the effects of offering tutoring sessions for students who fail core courses in their first semesters, examine the role of language skills in social science programs, and examine the role of the orientation year in other programs. Impact on Society: The prediction of academic performance can help both teachers and students in many ways. It also enables the early discovery of honorary students. Thus, well-deserved opportunities can be offered; for example, scholarships, internships, and workshops. It can also help identify students who require special attention to take an appropriate intervention at the earliest stage possible. Moreover, instructors can be aware of each student’s capability and customize the teaching tasks based on students’ needs. Future Research: For future work, the experiment can be repeated with a larger dataset. It could also be extended with more distinctive attributes to reach more accurate results that are useful for improving the students’ learning outcomes. Moreover, experiments could be done using other data mining algorithms to get a broader approach and more valuable and accurate outputs.

scholarly journals Methods of teaching educational data mining for pedagogical students

2019 ◽  
Vol 23 (3) ◽  
pp. 14-24
Author(s):  
E. A. Terbusheva
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Statistical Processing ◽  
Educational Process ◽  
Middle Level ◽  
Training Tool ◽  
Research Competence ◽  
Level Data ◽  
The Government

The aim of the article is to discuss and argue teaching educational data mining for pedagogical stu-dents and to describe the methodical system of educational data mining teaching for students with a middle level of mathematical and IT disciplines, that contributes to the development of student’s research competence. The relevance of the study is determined by the requirements for the ability of higher education graduates to analyze information and perform research using modern methods and technologies that are mentioned in the educational standards and the government order. They are associated with an increasing amount of accumulated data in various fields and the cost of the knowledge extracted from data. Materials and methods. The article describes the author’s methodical system of educational data mining teaching, which was developed rely on: analysis of requirements and expectations to the re-search competence level, data analysis skills and modern education in general; comparison and analysis of the content of educational programs, books and courses on data mining and related dis-ciplines, generalization of pedagogical experience. The main aspects underlying the methodology: a form of flipped learning, a concentric (iterative) content structure, research teaching methods, a set of practical tasks for developing research competencies and Weka software for data mining as the main technical training tool for practical tasks implementation. The effectiveness of the developed methodological system was tested by the educational process monitoring, students questioning and statistical processing of questionnaires data. Results. The study shows the relevance of educational data mining teaching for students of peda-gogical universities, studying in mathematical and informational specialization. The use of the de-scribed methodic system for senior pedagogical students allows increasing the level of research competence of students and significantly developing the competence of data analysis. Conclusion. The described methodical system can used be partially or completely by teachers and methodologists for teaching data analysis at the modern level and development of research compe-tence of students with an average level of knowledge in mathematical and IT disciplines. 

A Semi-Supervised Regression Algorithm for Grade Prediction of Students in Distance Learning Courses

2019 ◽  
Vol 28 (04) ◽  
pp. 1940001 ◽  
Author(s):  
Georgios Kostopoulos ◽  
Sotiris Kotsiantis ◽  
Nikos Fazakis ◽  
Giannis Koutsonikos ◽  
Christos Pierrakeas
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Undergraduate Students ◽  
Educational Data Mining ◽  
Predictive Performance ◽  
Educational Process ◽  
Training Set ◽  
Final Grade ◽  
Future Outcomes ◽  
Educational Field

Applying data mining methods in the educational field has gained a lot of attention among researchers in recent years. Educational Data Mining has turned into an effective tool for uncovering hidden relationships in educational data and predicting students’ learning outcomes. Several supervised methods have been successfully applied with the purpose of identifying students at risk of failing or of predicting their academic performance. Recently, the implementation of Semi-Supervised Learning (SSL) methods in the educational process indicated their superiority over the supervised ones. SSL is an emerging subfield of machine learning seeking to effectively exploit a small pool of labeled examples together with a large pool of unlabeled ones. On this basis, a small number of students’ data from previous years may be used as the training set of a learning model to predict future outcomes of current students. A number of rewarding studies deal with the implementation of classification methods in the educational field in contrast to regression, which is deemed to be a slightly touched task. In this paper, a novel semi-supervised regression (SSR) algorithm is presented for predicting the final grade of undergraduate students in a distance online course. To the best of our knowledge there is no study dealing with the implementation of SSR methods in the educational field. A plethora of attributes related to students’ characteristics, academic performance and interaction within the course online platform form the training set, while several experiments were carried out confirming the superiority of the proposed algorithm over familiar regression methods. The experiment results show that the predictive performance of the proposed algorithm is increasing significantly over time, achieving a MAE value of less than 1.2358 before the middle of the academic year, which provides the advantage of early warnings and interventions.

scholarly journals Theoretical Preconditions for [a] Change in the Student Assessment System: Toward Good Practices

2020 ◽  
Vol 8 ◽  
pp. 73-93
Author(s):  
Asta Volbikienė ◽  
Neringa Vilkaitė-Vaitonė ◽  
Remigijus Bubnys ◽  
Rūta Girdzijauskienė
Keyword(s):  
Change Management ◽  
Alternative Assessment ◽  
Portfolio Assessment ◽  
Good Practice ◽  
Student Assessment ◽  
Educational Process ◽  
Assessment System ◽  
Learning Activity ◽  
Success Experience ◽  
Teaching Learning

Student formal assessment is the core axis of the educational process that affects the whole teaching / learning activity, its quality, students’ success experience, their self-respect and self-esteem, and the perception of self-efficacy. By recognising prospects as the main learning objective and defining the outcome as personal and authentic learner progress, the assessment raises the need to pay a due attention to reflection, deep consideration, and feedback to all participants of the educational process. Against this background, doubts are started to be raised about appropriateness of the grade, currently being one of the most popular methods of the formal student assessment, leading to the scientific problem of this article. Over the last few decades, a shift in the assessment has been observed, from the focus exclusively on the end result to a stronger orientation toward the whole educational process, with an emphasis on motivating students to learn and strengthening their involvement in the educational process. These changes are illustrated by the Finnish good practice where, in an environment based on mutual trust and respect, and without questioning the importance and need for testing learners’ knowledge, abilities, and skills in the teaching / learning process, alternative assessment methods: portfolio assessment and learning conversations, are successfully used. To transfer examples of the good practice and adapt them to the national context, it seems reasonable to apply theories and models of change management. Achieving a targeted and effective change in the area of the assessment requires a process-focused approach to the change management.

Benefits of Educational Data Mining

2020 ◽  
Vol 6 (1) ◽  
pp. 12-16
Author(s):  
Alisa Bilal Zorić
Keyword(s):  
Data Mining ◽  
Teaching And Learning ◽  
Educational Data Mining ◽  
Large Data ◽  
Research Area ◽  
Large Data Sets ◽  
Data Sets ◽  
Use Of Data ◽  
Learning Conditions ◽  
Methods And Techniques

We live in a world where we collect huge amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. With new methods and techniques, we can use this data, analyze it and get a great advantage. The perfect method for this is data mining. Data mining is the process of extracting hidden and useful information and patterns from large data sets. Its application in various areas such as finance, telecommunications, healthcare, sales marketing, banking, etc. is already well known. In this paper, we want to introduce special use of data mining in education, called educational data mining. Educational Data Mining (EDM) is an interdisciplinary research area created as the application of data mining in the educational field. It uses different methods and techniques from machine learning, statistics, data mining and data analysis, to analyze data collected during teaching and learning. Educational Data Mining is the process of raw data transformation from large educational databases to useful and meaningful information which can be used for a better understanding of students and their learning conditions, improving teaching support as well as for decision making in educational systems.The goal of this paper is to introduce educational data mining and to present its application and benefits.

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