Association analysis of moodle e‐tests in blended learning educational environment

Dimić, Gabrijela; Predić, Bratislav; Rančić, Dejan; Petrovic, Vera; Maček, Nemanja; Spalević, Petar

doi:10.1002/cae.21894

Cited by 10 publications

(9 citation statements)

References 32 publications

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“…These authors concluded that LMSs must integrate tools and plugins; not only simple learning analytics, but also EDM tools that permit the use of supervised learning techniques (prediction) [36][37][38] and unsupervised learning techniques (classification) that provide a user-friendly interface for the teacher, which facilitates their use throughout the course of study. These plugins will help with the detection of at-risk students [41] and the implementation of actions to avoid academic failure and high dropout rates [42]. In line with these conclusions, the studies by Moreira Félix et al [37] and Dimić et al [42] placed special emphasis on the advantages of including a prediction module within the plugins in order to facilitate the academic monitoring of students.…”

Section: Characteristics Of the Learning Management System In The Detection Of The At-risk Studentmentioning

confidence: 89%

“…However, he concluded that Excel is not an ideal tool, as the number of rows and columns are a limitation for analysing student data. In turn, the studies by Luna et al [36] proposed the use of a Moodle plugin compatible with specific Educational Data Mining (EDM) techniques: supervised and unsupervised Machine Learning techniques [38][39][40][41][42]. These authors concluded that LMSs must integrate tools and plugins; not only simple learning analytics, but also EDM tools that permit the use of supervised learning techniques (prediction) [36][37][38] and unsupervised learning techniques (classification) that provide a user-friendly interface for the teacher, which facilitates their use throughout the course of study.…”

Section: Characteristics Of the Learning Management System In The Detection Of The At-risk Studentmentioning

confidence: 99%

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Monitoring Students at the University: Design and Application of a Moodle Plugin

Manzanares

García‐Osorio

2020

Applied Sciences

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Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities.

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Section: Characteristics Of the Learning Management System In The Detection Of The At-risk Studentmentioning

confidence: 89%

Section: Characteristics Of the Learning Management System In The Detection Of The At-risk Studentmentioning

confidence: 99%

Monitoring Students at the University: Design and Application of a Moodle Plugin

Manzanares

García‐Osorio

2020

Applied Sciences

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“…Dimić et al in ref. have applied Apriori and Predictive Apriori algorithms for providing feedback to instructors for improvement in the process of e‐testing. Aleem and Gore in ref.…”

Section: Related Workmentioning

confidence: 99%

The choice is yours: The effects of optional questions in engineering examinations

Aleem

Gore

2019

Comp Applic In Engineering

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Examiners may provide optional questions in the question paper of any examination. It gives examinees the choice to select their options to complete the set of the required number of questions for answering. The examinees may choose to answer the questions from the well-prepared portions of the subject.Some examinees misuse this facility by answering more than the required questions. Few examinees mess up the serial number of questions in the answer sheet. These acts of examinees sometimes create issues in marks awarding process, which raises doubt on the fairness of the evaluation process. This article reviews the contribution of optional questions in engineering semester examinations and analyzes the effects of improper behavior practiced by some of the examinees. Two surveys were conducted to gather perspectives on optional questions from examiners and examinees, respectively. The answering behavior of examinees vis-á-vis optional questions have been analyzed through the investigation of answer sheets, which were written for engineering semester examinations. Association rule mining methods have been applied on the gathered data to discover interesting hidden facts about tackling of optional questions. On the basis of findings, the article recommends further actions that need to be taken for effective utilization of optional question and question with optional subparts. The review of the effects of optional questions is a novel study and has not been dealt with in the literature for furthering the quality of engineering education. K E Y W O R D Sassociation rule mining, educational data mining, examination system, optional questions, question paper

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“…Dimic et al in ref. have utilized the three data matrices created in ref. to apply Apriori and Predictive Apriori algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…The feedback obtained in refs. are based on the students' response which does not consider the students' confidence while marking the answers.…”

Section: Related Workmentioning

confidence: 99%

C‐BEAM: A confidence‐based evaluation of MCQs for providing feedback to instructors

Aleem

Gore

2018

Comp Applic In Engineering

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Multiple‐choice questions (MCQs) examination is the most preferred methodology for evaluating knowledge in a specific context for a large number of students. The problem with MCQs is that students apply probabilistic approach to guess answers for scoring marks. Depending on the accuracy of the prediction, it may happen that the examinees who have little or no knowledge score more than or equal to the examinees who have better knowledge. Unexpected failures of the candidates who have passed MCQ based prestigious competitive examinations have also been observed. To overcome these contradictions, this article proposes a novel Confidence‐Based Evaluation Approach for MCQs (C‐BEAM) that curb the use of probabilistic prediction. The proposed marking scheme has been compared with well‐known marking schemes like no‐negative marking, one‐fourth negative marking, and one‐third negative marking. These marking schemes have been applied for the evaluation of eight MCQ quizzes on three engineering subjects that were attempted by more than 500 students at MNNIT Allahabad, India. The analysis of results proves C‐BEAM marking scheme as more effective than other compared methods in minimizing the effect of guessed answers. In addition to the effectiveness, C‐BEAM forms the basis for the confidence‐based feedback of students. This article also applies association rule mining methods to the evaluation data generated from C‐BEAM method for discovering association rules. These rules are confidence‐based feedback of students that would help instructors to improve the teaching process.

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Association analysis of moodle e‐tests in blended learning educational environment

Cited by 10 publications

References 32 publications

Monitoring Students at the University: Design and Application of a Moodle Plugin

Monitoring Students at the University: Design and Application of a Moodle Plugin

The choice is yours: The effects of optional questions in engineering examinations

C‐BEAM: A confidence‐based evaluation of MCQs for providing feedback to instructors

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