scholarly journals A Review on Prediction of Academic Performance of Students at-Risk Using Data Mining Techniques

2017 ◽  
Vol 5 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Preet Kamal ◽  
Sachin Ahuja
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
High Probability ◽  
Research Work ◽  
Educational Data Mining ◽  
Dropout Rate ◽  
Educational Background ◽  
Drop Out ◽  
Teaching Learning ◽  
The Right

Educational data mining is the procedure of converting raw data collected from educational databases into some useful information. It can be helpful in designing and answering research questions like performance prediction of students in academics, factors that affect the students’ performance, help the teachers in understanding the problems faced by the students to understand the course content and complexity of the subject taken so that the teachers can take timely action to control the dropout rate. This also includes improving the teaching learning process so that the interventions can be taken at the right time to improve the performance of the student. This paper is the review of the research work done in the field of educational data mining for the prediction of students’ performance. The factors that influence the performance of the students i.e. the type of classrooms they attend such as traditional or on-line, socio-economic, educational background of the family, attitude toward studies and challenges faced by the students during course progress. These factors leads to the categorization of the students into three groups “Low-Risk”: who have High probability of succeeding, “Medium-Risk”: who may succeed in their examination, “High-Risk”: who have High probability of failing or drop-out. It elaborates the different ways to improve the teaching learning process by providing the students personal assistance, notes, class-assignments and special class tests. The most efficient techniques that are used in educational data mining are also reviewed such as; classification, regression, clustering and and prediction.

scholarly journals Educational data mining in moodle data

2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Sushil Shrestha ◽  
Manish Pokharel
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Educational Data Mining ◽  
Feature Selection Method ◽  
Learning System ◽  
Significant Feature ◽  
Support Vector ◽  
Learning Platform ◽  
E Learning ◽  
Teaching Learning

<p>The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Assignment.Click, Chat.Click, File.Click, Forum.Click, System.Click, Url.Click, and Wiki.Click was used as the independent features and Grade as the dependent feature. Five classification algorithms such as K Nearest Neighbour, Naïve Bayes, and Support Vector Machine (SVM), Random Forest, and CART decision tree were applied in the moodle data. The finding shows that SVM has the highest accuracy in comparison to other algorithms. It suggested that File.Click and System.Click was the most significant feature. This type of research helps in the early identification of students’ performance. The growing popularity of the teaching-learning process through an online learning system has attracted researchers to work in the field of Educational Data Mining (EDM). Varieties of data are generated through several online activities that can be analyzed to understand the student’s performance which helps in the overall teaching-learning process. Academicians especially course instructors who use e-learning platforms for the delivery of the course contents and the learners who use these platforms are highly benefited from this research.</p>

THE PROCESSES AND STRATEGIES FOR DEVELOPING INNOVATIONS IN SCHOOLS

2016 ◽  
pp. 95-106
Author(s):  
B.L Raina
Keyword(s):  
Learning Process ◽  
Teacher Educators ◽  
Research Work ◽  
Educational Innovation ◽  
School Education ◽  
Educational Institutions ◽  
Individual Institution ◽  
Education In India ◽  
Teaching Learning

The paper attempts to discuss processes and strategies for innovations in schools. Committed and thoughtful teacher educators, translate their knowledge, expertise, skills and research work for bringing innovations in the teaching learning process in order to keep the system most engaged and updated. The notion of shared explicit philosophy of teaching learning is central, to innovations in the schools. There are mainly four responsible factors perceived in implementation of innovation in any organisation more so, in educational institutions, namely systems support, encouragement to creativity, autonomy and conformity. Fundamentals of innovations provide some insight in to the scope of educational innovation in school education in India. These are mostly based on unique personalised experiences of the learners and the learners retain centrality of focus. Rewards and recognition are the motivating force for fresh innovative ideas and practices. Individual, Institution and Implementation were three vital points for Innovation.

METHODOLOGICAL STRATEGY FOR THE RIGHT USE OF THE INTERACTIVE WHITEBOARD IN THE TEACHING-LEARNING PROCESS OF THE ENGLISH LANGUAGE IN THE UNIVERSITY

2017 ◽  
Author(s):  
Miguel Macías Loor ◽  
Roberth Zambrano Santos ◽  
José Intriago Macías ◽  
Juan Carlos Carpio ◽  
Marianela San Lucas Marcillo
Keyword(s):  
Learning Process ◽  
English Language ◽  
Interactive Whiteboard ◽  
Teaching Learning ◽  
The Right ◽  
The University ◽  
Methodological Strategy

scholarly journals Constructed Response or Multiple-Choice Questions for Assessing Declarative Programming Knowledge? That is the Question!

10.28945/4479 ◽  
2019 ◽  
Vol 18 ◽  
pp. 153-170
Author(s):  
Yolanda Belo ◽  
Sérgio Moro ◽  
António Martins ◽  
Pedro Ramos ◽  
Joana Martinho Costa ◽  
...  
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Multiple Choice ◽  
Added Value ◽  
Future Research ◽  
Declarative Programming ◽  
Multiple Choice Questions ◽  
Constructed Response ◽  
Programming Knowledge ◽  
Teaching Learning

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 teaching-learning 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. Recommendations for Practitioners: Teachers can make an informed decision about whether to choose multiple-choice questions or constructed-response taking into account the results of this study. Recommendation 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.

scholarly journals Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
Drop Out ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Student Graduation

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.

scholarly journals Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques

2020 ◽  
pp. 294-301
Author(s):  
Meenal Joshi ◽  
Shiv Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Secondary School ◽  
Student Performance ◽  
Learning Algorithms ◽  
Research Work ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Student’S Performance

<p>According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.</p>

scholarly journals Analysis on the Influence of The Epidemic on Teaching-Learning Process in Palghar Area

2021 ◽  
Vol 13 (SUP 1) ◽  
pp. 53-56
Author(s):  
Juita Tushar Raut ◽  
Vikram Patil
Keyword(s):  
Online Education ◽  
Learning Process ◽  
Teaching And Learning ◽  
Online Courses ◽  
The Novel ◽  
The Stability ◽  
Teaching Learning ◽  
The Right ◽  
The Impact ◽  
Back To Work

The unexpected outburst of the novel COVID-19, carried a lot of damage to whole world. To contain the epidemic, people had to stay where they were. They could not go back to work places or to school or colleges. The offline courses were due to many reasons infeasible, what brought unexpected changes to education. Aside from efforts to solve this co19 problem, the state must continue to maintain the stability and sustainability of the learning process that is the right of all citizens. India experienced the same thing. The online courses, learning process came into the picture. The influence focusses on the teaching and learning-effect, the transformation of the teaching forms. This paper mainly focussed on the impact of online-learning process on the parents. This research aims to determine how parents and their children feel about online education and also learn about their experiences.

Teaching English to Arab learners

2021 ◽  
Author(s):  
Ferid Chekili
Keyword(s):  
Learning Process ◽  
Foreign Languages ◽  
Linguistic Knowledge ◽  
Teaching English ◽  
Research Hypothesis ◽  
Educational Linguistics ◽  
Speed Up ◽  
Teaching Learning ◽  
The Right ◽  
Lack Of Knowledge

Abstract Both ‘Educational Linguistics’ and ‘Pedagogical Linguistics’ have demonstrated the importance of linguistic knowledge in teaching/learning second/foreign languages. More recently, there have been concrete proposals that insights from formal linguistics and theoretical acquisition research may also play a role in pedagogy. Indeed, many observed difficulties in L2 can be traced back to lack of knowledge, on the part of teachers, of certain abstract linguistic concepts. In this paper, two English constructions (constructions with Object pronouns and DP-internal concord) claimed to be problematic for the learner/teacher in the absence of any linguistic knowledge will be investigated in terms of their abstract properties. The implication is that such linguistic knowledge will speed up the process of learning. This will be supported by previous findings on aspectual contrasts in English and Arabic where such knowledge clearly obtains, causing the learning process to be relatively rapid. Evidence for the presence of this knowledge in the learners comes from observation of the transitional stages in the learning process which indicate that the learner is on the right track to learning. The research hypothesis will be argued to have significant implications for teaching, and thus, if correct, will corroborate some recent findings.

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