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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) EDUJAVARE: International Journal of Educational Research
Meily Ivane Esther Neman
Universitas Klabat, Minahasa Utara
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Students’ Perception of Teacher’s Code-Switching in Teaching English at Junior High School Marsanda Preysli Siwu; Meily Ivane Esther Neman
EDUJAVARE: International Journal of Educational Research Vol. 4 No. 02 (2026): EDUJAVARE: International Journal of Educational Research
Publisher : CV. Edujavare Publishing

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Abstract

This study aimed to investigate the level of students’ perceptions of teacher’s code-switching in teaching English at SMP UNKLAB. A descriptive quantitative design was employed, collecting data from 89 students through a questionnaire adapted from Aminah (2022). The findings revealed that the overall level of students’ perception was positive (M = 3.92). Specifically, the results for each indicator were interpreted as positive: receiving of stimulus (M = 3.92), understanding (M = 4.00), and evaluation (M = 3.86). These results indicate that students find teacher’s code-switching to be a helpful strategy that supports their comprehension and motivation in class. In conclusion, code-switching is perceived as a valuable instructional tool to bridge language barriers. Therefore, it is recommended that English teachers use code-switching strategically to clarify complex materials while maintaining sufficient English exposure for students.
Comparing Machine Learning and BERT for Classifying Grammatical Error Types in Indonesian EFL Essays Meily Ivane Esther Neman; George Morris William Tangka; Regi Fernando Najoan; Enrico Samuel Djimesha; Daniel Semuel Warouw
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11296

Abstract

Automated writing feedback tools are usually trained on learner corpora from outside Indonesia, so their accuracy on Indonesian students' grammatical errors is unknown. This study compares Multinomial Naive Bayes, a linear Support Vector Machine (SVM), Random Forest, and a fine-tuned BERT model for classifying nine grammatical error types. The models were trained on 48,011 error edits from the BEA-2019 W&I+LOCNESS and FCE corpora, re-annotated with ERRANT 3, and tested in-domain on 5,129 edits and cross-corpus on 1,900 edits from 66 essays by 33 Indonesian university students in the ICNALE Edited Essays. BERT achieved the highest macro-F1 on both test sets, 0.917 in-domain and 0.849 on the Indonesian essays, and McNemar tests confirmed its advantage over each classical model. All models lost 0.035 to 0.068 macro-F1 under the corpus shift. Noun number errors formed 24% of the Indonesian errors, three times their training share, while word form and pronoun were the hardest classes. This study contributes a cross-corpus benchmark of classical and Transformer-based error-type classifiers on Indonesian students' essays, a reproducible pipeline that labels both corpora with the same ERRANT version, and a class-level reliability profile for classroom feedback. For English teachers in Indonesia, the results support explicit, contrastive practice on articles and plural marking and the use of BERT for immediate feedback on reliably recognised classes, while its word form and pronoun suggestions are confirmed by the teacher.
Co-Authors Daniel Semuel Warouw Enrico Samuel Djimesha George Morris William Tangka Marsanda Preysli Siwu Regi Fernando Najoan