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Academic Resilience and Science Academic Emotion in Numeration under Online Learning: Predictive Capacity of an Artificial Neural Network

Jurnal Pendidikan IPA Indonesia · Vol. 11 No. 4 (2022) · pp. 542–551

DOI
10.15294/jpii.v11i4.39091
ISSN
2089-4392, 2339-1286
Published
2022-12-29
Publisher
Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)
Language
eng

Abstract

The main objective of this study is to predict student academic resilience based on academic emotions in studying numeration and science under online learning. Many researchers have analyzed student academic resilience and online learning. Unfortunately, only a few similar research topics focus on numeration and science. 191 students at a university in Central Java Province have been randomly selected as research samples. Academic resilience is classified into three groups: low, medium, and high. The academic emotions were measured using three indicators: class-related emotions, learning-related emotions, and test emotions. This study uses an artificial neural network (ANN) to obtain predictive values. The results indicate that the level of academic resilience and academic emotion in numeration and science under online learning is in the medium category. The results also show that the relative error provides a fairly small percentage, namely 19.7% at the training stage and 25% at the testing stage. This refers to the prediction results having a good level of accuracy. Predictive estimation results also indicate that class-related emotions are predicted to be the aspect that has the most crucial impact on students’ academic resilience, in which the normalized importance value is 100.0%. It is followed by the aspect of learning-related emotions (65.0%) and test emotions (24.3%). The implication is that the aspect of class-related emotions should get better attention from lecturers and students so that students can increase their chances of getting a good level of academic resilience in numeration and science.

Keywords

Collaboration network of the authors

6 authors · 9 co-authorship links · 7 across different organisations

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  • Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan
  • Universiti Malaysia Terengganu
  • Universitas Nahdlatul Ulama Indonesia
  • Sebelas Maret University
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Suhartono SuhartonoInstitut Teknologi Sepuluh Nopember1
Ade Dedi RohayanaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/nju/index.php/jpii/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Academic Resilience and Science Academic Emotion in Numeration under Online Learning: Predictive Capacity of an Artificial Neural Network</dc:title>
	<dc:creator>Mahmudah, U.</dc:creator>
	<dc:creator>Lola, M. S.</dc:creator>
	<dc:creator>Fatimah, S.</dc:creator>
	<dc:creator>Suryandari, K. C.</dc:creator>
	<dc:subject xml:lang="en-US">academic resilience; artificial neural network; numeration; online learning; science emotion</dc:subject>
	<dc:description xml:lang="en-US">The main objective of this study is to predict student academic resilience based on academic emotions in studying numeration and science under online learning. Many researchers have analyzed student academic resilience and online learning. Unfortunately, only a few similar research topics focus on numeration and science. 191 students at a university in Central Java Province have been randomly selected as research samples. Academic resilience is classified into three groups: low, medium, and high. The academic emotions were measured using three indicators: class-related emotions, learning-related emotions, and test emotions. This study uses an artificial neural network (ANN) to obtain predictive values. The results indicate that the level of academic resilience and academic emotion in numeration and science under online learning is in the medium category. The results also show that the relative error provides a fairly small percentage, namely 19.7% at the training stage and 25% at the testing stage. This refers to the prediction results having a good level of accuracy. Predictive estimation results also indicate that class-related emotions are predicted to be the aspect that has the most crucial impact on students’ academic resilience, in which the normalized importance value is 100.0%. It is followed by the aspect of learning-related emotions (65.0%) and test emotions (24.3%). The implication is that the aspect of class-related emotions should get better attention from lecturers and students so that students can increase their chances of getting a good level of academic resilience in numeration and science.</dc:description>
	<dc:publisher xml:lang="en-US">Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-12-29</dc:date>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/nju/jpii/article/view/39091</dc:identifier>
	<dc:identifier>10.15294/jpii.v11i4.39091</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Pendidikan IPA Indonesia; Vol 11, No 4 (2022): December 2022; 542-551</dc:source>
	<dc:source>2089-4392</dc:source>
	<dc:source>2339-1286</dc:source>
	<dc:source>10.15294/jpii.v11i4</dc:source>
	<dc:language>eng</dc:language>
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	<dc:relation>https://journal.unnes.ac.id/nju/jpii/article/downloadSuppFile/39091/11168</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Jurnal Pendidikan IPA Indonesia</dc:rights>
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