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Optimal Scale Points for Reliable Measurements: Exploring the Impact of Scale Point Variation Ismail, Raoda; Retnawati, Heri; Sugiman, Sugiman; Setiawati, Farida Agus; Imawan, Okky Riswandha; Santoso, Purwoko Haryadi
JP3I (Jurnal Pengukuran Psikologi dan Pendidikan Indonesia) Vol 13, No 1 (2024): JP3I
Publisher : Fakultas Psikologi UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jp3i.v13i1.34173

Ensuring reliable measurements is crucial for minimising errors in assessments. The assessmentcommunity commonly employs the evaluation of reliability coefficients to estimate the dependability oftest scores. Despite its significance, limited research has explored the relationship between the estimated reliability coefficient and the number of scale points utilised. This study aims to provide valuable insights to practitioners by investigating the optimal number of scale points required for the most accurate reliability coefficient estimation. Using simulated data, the research scrutinises scales with varying points, ranging from 2 to 11. The results reveal a substantial impact of the number of scale points on reliability estimation. The most accurate estimate of reliability is obtained for scales with 8 points. This study helps us understand the optimal number of scale points for reliable measurements and guides future assessment improvements.

Predicting Physics Students’ Achievement Using In-Class Assessment Data: A Comparison of Two Machine Learning Models Santoso, Purwoko Haryadi; Santosa, Hayang Sugeng; Istiyono, Edi; Haryanto, Haryanto; Retnawati, Heri
Physics Education Research Journal Vol 5, No 2 (2023)
Publisher : Faculty of Science and Education, UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/perj.2023.5.2.14217

Data is the primary source to scaffold physics teaching and learning for teachers and students, mainly reported through in-class assessment. Machine learning (ML) is an axis of artificial intelligence (AI) study that immensely attracts the development of physics education research (PER). ML is built to predict students’ learning that can support students’ success in an effective physics achievement. In this paper, two ML algorithms, logistic regression and random forest, were trained and compared to predict students’ achievement in high school physics (N = 197). Data on students’ achievement was harvested from in-class assessments administered by a physics teacher regarding knowledge (cognitive) and psychomotor during the 2020/2021 academic year. Three assessment points of knowledge and psychomotor were employed to predict students’ achievement on a dichotomous scale on the final term examination. Combining in-class assessment of knowledge and psychomotor, we could discover the plausible performance of students’ achievement prediction using the two algorithms. Knowledge assessment was a determinant in predicting high school physics students’ achievement. Findings reported by this paper recommended open room for the implementation of ML for educational practice and its potential contribution to supporting physics teaching and learning.

A study of the readiness of post-pandemic computer-based four-tier diagnostic test (CBFTDT): A review of economic level, school grades, and device accessibility Istiyono, Edi; Dwandaru, Wipsar Sunu Brams; Ayub, Made Rai Shanti; Saepuzaman, Duden; Zakwandi, Rizki; Rachman, Anisyah; Yusron, Eri; Santoso, Purwoko Haryadi
Jurnal Penelitian dan Evaluasi Pendidikan Vol 27, No 1 (2023)
Publisher : Graduate School, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pep.v27i1.56005

Student readiness for using computer-based assessment was affected by economic level, school grade, and device accessibility. This study analyzed student readiness using a computer-based assessment, the four-tier diagnostic test (CBFTDT). The data in this study were students' responses to a questionnaire consisting of 16 statements about three aspects: mental access, skill access, and usage. The questionnaire consists of 16 statements with three aspects: items, media, and effectivity. The questionnaire was proved valid by experts and its estimated reliability score was 0.99. This study proved that the Student is still not ready to use the computer-based assessment, with an average readiness of 60%. In addition, there is no effect of economic level, school grade, and device accessibility on the readiness for using the computer-based assessment. Hence, the treatment of all stockholders is needed to prepare students to use the computer-based assessment.

Karakterisasi Muatan Nanopartikel Silika (SiO2) dengan Metode Elektroforesis Purwoko Haryadi Santoso; Yohanes Kurniawan; Havid Noor Pamungkas; Suparno Suparno
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 11, No 1 (2021): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v11i1.48326

Electrophoresis is one of the experimental methods employed in this study to characterize unique properties of charges of silica nanoparticles (SiO2) by observing their electrophoretic phenomena while they are situated in the electric field. This study is aimed to measure one of the SiO2 properties, namely the charge, using electrophoresis method through the variation of electric fields. The charge dependencies of SiO2 was probed towards five times variation of electric fields 1000, 1250, 1500, 1750, and 2000 V/m in 20 mL of aquades. The displacement of SiO2 could be observed through the light microscope with 160x magnification which the recorded observations then were analyzed by timeline-based software to measure the displacement time of particles during the observation. The results revealed that silica nanoparticles have the kind of positive charges in the colloidal solution. It is caused the magnitude of SiO2 charges is ranged constantly despite the variational effect of electric field in the environment. Light microscope has been optimized in this study to measure the velocity of SiO2 that tends to increase with respect to the magnifying electric fields given in the experiment.

Approaching electrical circuit understanding with circuit builder virtual laboratory Santoso, Purwoko Haryadi; Munawanto, Nino
Jurnal Ilmiah Pendidikan Fisika Al-Biruni Vol 9 No 2 (2020): Jurnal Ilmiah Pendidikan Fisika Al-Biruni
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/jipfalbiruni.v9i2.5976

Nowadays, most students are familiar with the notebook for daily use. The compactness and flexibility of the notebook offer more benefits than a PC. The teachers can engage it during teaching physics. Hence, this research had developed an electrical circuit virtual experiment that acts like real experiments. It can be accessed easily by students in their notebook. This study introduces a flash-based animation Circuit Builder as a simulation designed to help students understand the electrical circuit. This study's purposes were: (1) to analyze the feasibility level of Circuit Builder for enhancing students’ electrical circuit mastery and (2) to know the effectiveness of Circuit Builder based on students’ electrical circuit mastery. Circuit Builder was developed by 4D (Define, Design, Develop, and Disseminate) model. The feasibility level was analyzed by CVI (Content Validity Index). Then, the effectiveness was tested with effect size. This study proved that the virtual laboratory "Circuit Builder” was feasible in physics class with a moderate effect size. The virtual laboratory could improve students’ electrical circuit mastery than doing practices with traditional laboratories.

CAPTURING RANDOM-EFFECT META-ANALYSIS TOWARD SCIENTIFIC INQUIRY LEARNING APPROACH IN SCIENCE EDUCATION Mobinta Kusuma; Insih Wilujeng; Purwo Susongko; Heri Retnawati; Purwoko Haryadi Santoso; Chokchai Yuenyong; Ariyatun Ariyatun
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i3.35600

Implementing scientific inquiry as a learning approach has accounted for most discussion topics and research areas in science education. Numerous studies have been conducted for decades; therefore, synthesizing the updated results will be worthwhile. Diverse needs require effective implementation of the vision of scientific inquiry for scientists, and various forms of learning output pose a challenge for scholars to provide a systematic summary of evidence regarding its role in science teaching and learning. Therefore, the random effect model was suitable for this paper to capture the impact of scientific inquiry on science learning comprehensively. A meta-analysis study using a random-effects model was chosen to systematically synthesize 22 academic articles gathered from the Scopus and Web of Science indexing databases. An individual paper was first extracted for its sample size, mean, and corresponding standard deviation, which were then calculated to measure the effect size of each piece of evidence using the JASP program. In summary, we highlight the positive and moderate impact of scientific investigation on student learning outcomes as a promising approach to enhancing science learning. This study fills the gap in the previous literature by providing a cross-cultural systematic synthesis of Indonesian and non-Indonesian literature, as well as comprehensively measuring the effects of scientific inquiry interventions. These findings support scientific inquiry as a promising approach to learning and encourage the sustainability of efforts to enhance the quality of science learning in various educational contexts.

Predicting Physics Students’ Achievement Using In-Class Assessment Data: A Comparison of Two Machine Learning Models Santoso, Purwoko Haryadi; Santosa, Hayang Sugeng; Istiyono, Edi; Haryanto, Haryanto; Retnawati, Heri
Physics Education Research Journal Vol. 5 No. 2 (2023)
Publisher : Faculty of Science and Education, UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/perj.2023.5.2.14217

Data is the primary source to scaffold physics teaching and learning for teachers and students, mainly reported through in-class assessment. Machine learning (ML) is an axis of artificial intelligence (AI) study that immensely attracts the development of physics education research (PER). ML is built to predict students’ learning that can support students’ success in an effective physics achievement. In this paper, two ML algorithms, logistic regression and random forest, were trained and compared to predict students’ achievement in high school physics (N = 197). Data on students’ achievement was harvested from in-class assessments administered by a physics teacher regarding knowledge (cognitive) and psychomotor during the 2020/2021 academic year. Three assessment points of knowledge and psychomotor were employed to predict students’ achievement on a dichotomous scale on the final term examination. Combining in-class assessment of knowledge and psychomotor, we could discover the plausible performance of students’ achievement prediction using the two algorithms. Knowledge assessment was a determinant in predicting high school physics students’ achievement. Findings reported by this paper recommended open room for the implementation of ML for educational practice and its potential contribution to supporting physics teaching and learning.

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