Improving Quality of Educational Processes Providing New Knowledge Using Data Mining Techniques

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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“…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%

“…The feedback obtained in refs. [7,9,12,26] are based on the students' response which does not consider the students' confidence while marking the answers. As per our knowledge and understanding, no other work has been reported for the application of ARM on the response data of quiz or questionnaire.…”

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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“…Penggunaan data mining ini dapat berguna sekali untuk dijadikan sebagai alat bantu strategis dalam menunjang keputusan berdasarkan data pendidikan untuk mengatasi tantangan yang sangat sulit dan penting untuk meningkatkan kualitas proses pendidikan [3]. Beberapa penelitian penerapan data mining telah dilakukan.…”

Section: Pendahuluanunclassified

Peningkatan Akurasi Klasifikasi Tingkat Penguasaan Materi Bahan Ajar Menggunakan Jaringan Syaraf Tiruan Dan Algoritma Genetika

Somantri

Wiyono

2017

J. Teknol. dan Sist. Komput.

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-Decision support systems can be applied to perform a lecturer's performance assessment. This research aims to develop a hybrid model using the artificial neural network (ANN) and genetic algorithm (GA) that can be implemented and used as a model of decision support data analysis that produce better accuracy, specifically to assess the lecturer's comprehension of their teaching materials. The use of GA in determining the ANN parameter has increased the accuracy from 85.36% to 85.73%. The training cycle is also reduced to 624 from 1000. Kinerja seorang dosen dapat dinilai dari beberapa aspek penilaian, di antaranya penilaian internal institusi melalui DP3 atau penilaian-penilaian lain yang bisa dijadikan sebagai informasi pendukung keputusan dalam menilai dosen apakah berkinerja baik atau tidak. Salah satu penilaian lain adalah dengan berdasarkan pada penilaian hasil umpan balik mahasiswa terhadap kinerja dosen. Kinerja dosen adalah unjuk kerja yng diperlihatkan atau ditampilkan dosen dalam melaksanakan tugasnya pada proses pembelajaran berdasarkan tingkat kualitas proses pembelajaran dan penilaian subjektivitas mahasiswa atau tingkat kualitas dari proses yang sistematik dan kompleks yang terdiri atas: (1) cara penyampaian materi kuliah, (2) cara berkomunikasi, (3) kreativitas dosen, (4) disiplin dosen, (5) penilaian terhadap hasil karya mahasiswa, dan (6) kepuasan terhadap sarana dan prasarana yang mendukung secara langsung maupun tidak langsung dalam proses pembelajaran [1].Penilaian kinerja dosen yang dilakukan harus bisa menjadi sebuah pendukung keputusan bagi para pengambil keputusan di instansi homebase dosen yang bersangkutan dalam memutuskan nilai kinerja dosen tersebut. Masih sulitnya dalam memperoleh informasi yang akurat dalam memutuskan penilaian kinerja dosen akan sangat menyulitkan para pengambil keputusan, dalam hal ini pimpinan perguruan tinggi, untuk menilai kinerja dosen yang bersangkutan. Untuk mengatasi permasalahan tersebut sangat diperlukan sebuah metode analisis yang tepat untuk bisa menjadi solusi yang terbaik sebagai pendukung keputusan satunya adanya penilaian terhadap penguasaan dosen terhadap bahan ajar yang akan diajarkannya pada mahasiswa.Penelitian mengenai kinerja dosen telah banyak dilakukan, salah satunya adalah penelitian mengenai

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“…The use of data mining techniques in educational data becomes a useful strategic tool in dealing with difficult and crucial challenges to improve the quality of education by supporting higher education institutions in the decision making process [1]. Educational data mining (EDM) is a method to explore large data derived from educational data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Decision Tree and Naïve Bayes Classifier for Predicting Study Period and Predicate of Student’s Graduation

Renaningtias¹,

Endro²,

Gernowo³

2018

IJCA

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One of the biggest challenges that faces by institutionsof the higher education is to improve the quality of the educational system. This problem can be solved by managing student data at institutionsof higher education to discover hidden patterns and knowledge by designing an information system. This study aims to designing an information system based on hybrid decision tree and naïve bayes classifier to predict the study period and predicate of graduated. The data are used in this research such as the Grade Point Average (GPA) from early 2 semesters, type of entrance examinations, origin of the high school, origin of the city, major in high school, gender, scholarship and relationship status amounting to 215 sets of data. The learning process is done by using hybrid of decision tree C4.5 algorithm and naive bayes classifier with data partition 70%, 80% and 90%. The results found that using a 90% data partition gives a higher accuracy score of 72.73% in predicting the study period and predicate of graduation predicate. General TermsPredicting study period and predicate of graduated KeywordsData mining, decision tree, naïve bayes classifier, NBTree, and student performance.

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Improving Quality of Educational Processes Providing New Knowledge Using Data Mining Techniques

Cited by 58 publications

References 3 publications

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

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

Peningkatan Akurasi Klasifikasi Tingkat Penguasaan Materi Bahan Ajar Menggunakan Jaringan Syaraf Tiruan Dan Algoritma Genetika

Hybrid Decision Tree and Naïve Bayes Classifier for Predicting Study Period and Predicate of Student’s Graduation

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