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31,456 Article Results

A new protist literacy assessment for university students

10.11591/ijere.v14i1.31293
Hardianto Hardianto , Susriyati Mahanal , Hendra Susanto , Sitoresmi Prabaningtyas , Siti Zubaidah
Protists are organisms that have close involvement with student life, especially in maintaining the balance of ecosystems on earth. Literacy instruments about protists are important to develop because students need to know the relationship between protists and life. This instrument was developed because there is no specific instrument to measure student protist literacy. The purpose of this study is to develop a new protist literacy assessment (N-PLA) for university students. The N-PLA development process consists of three stages, namely, i) dimension development, item development, and expert validation; ii) exploratory factor analysis (EFA), content validity, and data reduction; iii) internal consistency through confirmatory factor analysis (CFA). The results of the study found seven dimensions of protist literacy with a total of 36 items: conceptual knowledge (CnK) (7 item), relation information (RlI) (6 item), fact evaluation (FcE) (5 item), real solution (RlS) (5 item), argument identification (ArI) (3 item), self-confidence (S-C) (4 item), and scientific value (ScV) (6 item). The developed N-PLA has a relationship between dimensions and items based on EFA and CFA test results. N-PLA has two types of instruments, namely by using tests and questionnaires to be able to measure the cognitive and affective of students. N-PLA is recommended as a measuring tool that can measure the protist literacy of university students. The results of measuring student’s protist literacy become a reference in empowering students protist literacy in the learning process in university.
Volume: 14
Issue: 1
Page: 612-620
Publish at: 2025-02-01

Malay essay writing module based on thematic approach for non-native speaker: a need analysis in primary schools

10.11591/ijere.v14i1.31040
Edmund Austrus , Zamri Mahamod , Nor Hafizah Adnan
The challenges faced by non-native speaking pupils in primary schools, characterized by diverse backgrounds, highlight the difficulty in teaching and learning essay writing. This study was conducted to identify the needs for the development of a Malay language essay writing module based on a thematic approach through the needs analysis phase. This qualitative study was conducted among Malay language teachers in Sarawak. Eight teachers were purposively sampled, and thematic analysis was employed for data analysis. The findings found that there are six themes that have been identified, namely: i) relevant form of teaching and learning in essay writing; ii) relevant teaching and learning resources; iii) relevant teaching and learning activities; iv) relevant teaching and learning strategies; and v) the needs of the thematic approach in essay writing; vi) the needs for the development of thematic-based essay writing module. The findings underscore the imperative development of a Malay essay writing module based on a thematic approach to enhance the essay writing proficiency of non-native-speaking pupils. Furthermore, the study's insights offer valuable guidance for researchers in designing and developing modules during the subsequent phases, contributing to the refinement of essay writing pedagogy in primary schools.
Volume: 14
Issue: 1
Page: 103-113
Publish at: 2025-02-01

The influence of perceive teacher support on high school students’ learning engagement in China

10.11591/ijere.v14i1.29898
Liu Yang , Lim Hooi Lian
This study explores the influence of perceived teacher support on learning engagement and the mediating role played by psychological capital of senior high school students. There were 572 Chinese senior high school students from Inner Mongolia Autonomous Region in China completed a ranger of self-report questionnaires. The results of this study showed that perceived teacher support was significantly and positively related to learning engagement of senior high school students, and positively correlated with their psychological capital which consists of four dimensions: hope, self-efficacy, optimism, and resilience. The psychological capital was significantly and positively associated with learning engagement among senior high school students and mediated the relationships between perceived teacher support and learning engagement.
Volume: 14
Issue: 1
Page: 575-582
Publish at: 2025-02-01

Performance analysis of a neuromodel for breast histopathology decision support system

10.11591/ijai.v14.i1.pp102-108
Adedayo Olukayode Ojo , Adetoro Mayowa Sola , Florence Omolara Ojo , Moses Onibonoje Oluwafemi
Breast cancer detection and diagnosis are crucial in reducing mortality rates among women globally. This research article explores an artificial intelligence technique for early breast cancer detection, aiding doctors in making informed decisions for improved patient management. The study employs histopathological analysis of breast tissue microscopically to detect abnormalities, with the aim of categorizing normal tissue, benign lesions, in situ carcinoma, and invasive carcinoma. The proposed technique utilizes an artificial neural network trained using the resilient backpropagation algorithm (RP_ANN). The study further compares the observed performance with those of three other algorithms, including gradient descent algorithm (GDA_ANN), Levenberg-Marquardt algorithm (LM_ANN), and layer sensitivity-based (LSB_ANN) algorithm based on various evaluation metrics. RP_ANN and LSB_ANN demonstrated superior performance, with high validation and training variance accounted for (VAF) and low root mean squared error (RMSE). The results underscore the potential of deep learning-based algorithms for improving breast cancer detection, promising better patient outcomes and enhanced diagnostic accuracy.
Volume: 14
Issue: 1
Page: 102-108
Publish at: 2025-02-01

Effects of professional learning communities on job satisfaction of female teachers in Vietnam

10.11591/ijere.v14i1.30232
Nguyen Thanh Ly , Nguyen Van Hong
This study focuses on this topic to better understand how professional learning communities (PLCs) affect the work satisfaction of female primary school teachers in Vietnam. By utilizing the Statistical Package for the Social Sciences (SPSS 26) to evaluate our sample of 675 female primary school teachers, we were able to provide insight into the efficacy of PLCs in enhancing work satisfaction levels. The findings show that, among this population, participation in PLCs is significantly correlated with higher job satisfaction. In which, the collective learning and application (LA) element is followed by the shared and supportive leadership (SS), supportive conditions—relationships (SR), supportive conditions—structures (CS), and shared personal practice (PP) elements, shared values and vision (VV) has the most minor influence. Using PLCs as a workable solution to improve female educators’ working conditions and job satisfaction emphasizes the possible advantages for educational practice and policy.
Volume: 14
Issue: 1
Page: 85-93
Publish at: 2025-02-01

Machine learning-assisted decision support in industrial manufacturing: a case study on injection molding machine selection

10.11591/ijai.v14.i1.pp270-285
Faouzi Tayalati , Soulaimane Idiri , Ikhlass Boukrouh , Abdellah Azmani , Monir Azman
Selecting the right injection molding machine for new products remains a challenging task that significantly influences the profitability and flexibility of companies. The conventional approach involves performing theoretical calculations for clamping force, conducting mechanical validations of the mold, and carrying out real trials for new parts. This approach is time-consuming, costly, and requires a high level of expertise to ensure the optimal machine choice. This study explores the use of machine learning (ML) methods for efficient machine selection based on product, material, and mold criteria. Six supervised learning techniques were tested on a dataset comprising 70 plastic parts and five machines. Evaluation metrics like F1-score, recall, precision, and accuracy were used to compare models. The results indicate that ML can provide guidance for predicting machine selection, with a preference for the random forest (RF), decision tree (DT), and support vector machine (SVM) models. The most favorable outcome is demonstrated by the RF model, displaying an accuracy of 93%. In this manner, these findings may be helpful for injection molding businesses that are considering the significance of using classification algorithms in their manufacturing process. 
Volume: 14
Issue: 1
Page: 270-285
Publish at: 2025-02-01

Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach

10.11591/ijai.v14.i1.pp737-748
Silviya Hasana , Wina Permana Sari , Rojali Rojali , Devi Fitrianah
Due to their extensive volume and range of features, seismic data is regarded as highly complex data. Earthquakes that typically composed of foreshocks, mainshocks, and aftershocks, exhibit a unique sensitivity to temporal dimension, a characteristic that differs them from other natural hazards. Foreshocks and aftershocks that emanate from a similar epicenter, often display temporal patterns that contribute significantly to determining a sequence. This study introduces a density cube-based approach to cluster spatiotemporal seismic data. It addresses spatial irregularities observed in earthquake clusters and incorporates temporal aspects, acknowledging that seismic events originating from a similar epicenter could occur in separate time frames. We achieved the highest Silhouette score of 0.935 in daily-based clustering and 0.782 in weekly-based clustering. Notably, our analysis reveals a trend where weekly clustering lambda λ tend to be lower (λ=0.01) than in daily clustering (λ=0.1, λ=0.5), thus emphasizing the significance of temporal granularity where daily clustering requires higher λ to capture rapid fluctuations, while weekly clustering benefits from lower λ to cover broader trends. These findings enhance the understanding of the nuanced interplay of temporal dynamics in seismic sequence analysis.
Volume: 14
Issue: 1
Page: 737-748
Publish at: 2025-02-01

Collaborative praxis learning to empower rural area: an action research

10.11591/ijere.v14i1.27716
Abdul Aziz , Ahmad Barizi , Syuhadak Syuhadak , Kususanto Ditto Prihadi , Hamim Hamim , Eliya Nur khayati , Syaiful Mustofa
The emergence of collaborative learning praxis among adult learners has gained prominence, fostering lifelong practical learning. This approach empowers adults to define their learning goals based on interests and needs, facilitating contextual information sharing, knowledge acquisition, and skill enhancement. Such an approach promotes innovation and creativity, strengthening market competitiveness. This study focuses on collaborative learning’s impact in the village of Sukodono, East Java, Indonesia, post-COVID-19. Collaborative learning facilitated skill development, innovation, and market influence. Action research was employed to restore economic well-being. Themes of healthy environment preservation, community self-help, and market power were explored. The villagers’ adoption of biogas technology, digital platforms for product promotion, and focus on high-demand products showcased the efficacy of collaborative learning. This praxis enhances economic conditions and knowledge acquisition, enabling sustainable community development. Further quantitative studies are recommended to assess long-term impacts.
Volume: 14
Issue: 1
Page: 749-757
Publish at: 2025-02-01

Human motion classification by micro-doppler radar using intelligent algorithms

10.11591/ijece.v15i1.pp455-466
Andres Felipe Arias Ballen , Edith Paola Estupiñan Cuesta , Juan Carlos Martinez Quintero
This article introduces a technique for detecting four human movements using micro-doppler radar and intelligent algorithms. Micro-doppler radar exhibits the capability to detect and measure object movements with intricate detail, even capturing complex or non-rigid motions, while accurately identifying direction, velocity, and motion patterns. The application of intelligent algorithms enhances detection efficiency and reduces false alarms by discerning subtle movement patterns, thereby facilitating more accurate detection and a deeper understanding of observed object dynamics. A continuous wave radar setup was implemented utilizing a spectrum analyzer and radio frequency (RF) generator capturing signals in a spectrogram centered at 2,395 MHz. Six models were assessed for image classification: VGG-16, VGG-19, MobileNet, MobileNet V2, Xception, and Inception V3. A dataset comprising 500 images depicting four movements-running, walking, arm raising, and jumping-was curated. Our findings reveal that the most optimal architecture in terms of training time, accuracy, and loss is VGG-16, achieving an accuracy of 96%. Furthermore, precision values of 96%, 100%, and 98% were obtained for the movements of walking, running, and arm raising, respectively. Notably, VGG-16 exhibited a training loss of 4.191E-04, attributed to the utilization of the Adam optimizer with a learning rate of 0.001 over 15 epochs and a batch size of 32.
Volume: 15
Issue: 1
Page: 455-466
Publish at: 2025-02-01

Imitation of the human upper limb by convolutional neural networks

10.11591/ijai.v14.i1.pp193-203
Paula Useche Murillo , Robinson Jimenez Moreno , Javier Eduardo Martinez Baquero
The paper outlines the development of an algorithm focused on imitating movements of a human arm and replicating strokes generated by the user's hand within a working environment. The algorithm was crafted to discern the position of either the user's left or right arm, tracking each section (fingers, wrist, elbow, and shoulder) through a detection and tracking system. These movements are then replicated onto a virtual arm, simulating the actions of a cutting tool, generating strokes as it moves. Convolutional neural networks (CNNs) were employed to detect and classify each arm section, while geometric analysis determined the rotation angles of each joint, facilitating the virtual robot's motion. The stroke replication program achieved an 84.2% accuracy in stroke execution, gauged by the closure of the polygon, distance between initial and final drawing points, and generated noise, which was under 10%, with a 99% probability of drawing a closed polygon. A Fast region-based convolutional neural network (Fast R-CNN) network detected each arm section with 60.2% accuracy, producing detection boxes with precision ranging from 17% to 59%. Any recognition shortcomings were addressed through mathematical estimation of missing points and noise filters, resulting in a 90.4% imitation rate of human upper limb movement.
Volume: 14
Issue: 1
Page: 193-203
Publish at: 2025-02-01

Higher order thinking skills research trends: a bibliometric analysis in selected journals (2014 to 2023)

10.11591/ijere.v14i1.29590
Lilis Lismaya , Hartono Hartono , Bambang Subali , Woro Sumarni , Saiful Ridlo , Murbangun Nuswowati
This article aims to analyze the trend of higher order thinking skills (HOTS) research in selected journals, namely Journal of Research in Science Teaching (JRST), International Journal of Science Education (IJSE) and Review of Educational Research (RER) through bibliometrics on the Scopus database from 2014 to 2023. The articles consist of a total of 947, then divided into 333 articles analyzed, with details of JRST 113 articles, IJSE 130 articles and RER 90 articles. The method used in this research is article analysis using bibliometric stages, followed by keyword search, initial search results, correction of search results, initial data compilation and data analysis. The results showed that the publication of scientific articles in selected journals on HOTS varied from year to year. The results of this study are expected to help other researchers who are interested in reviewing and researching research trends related to HOTS in selected journals and to recommend further research directions.
Volume: 14
Issue: 1
Page: 695-707
Publish at: 2025-02-01

Authentic assessment activities for oral Malay language skills in primary schools

10.11591/ijere.v14i1.32079
Norliza Ramlan , Wan Muna Ruzanna Wan Mohammad , Zamri Mahamod
The quality of assessment conducted by teachers plays an important role in ensuring students’ mastery of Malay language skills, particularly oral skills. Assessment through direct activities can highlight students’ true potential and skills. Therefore, this study aims to explore the forms of authentic assessment activities for Malay language oral skills in primary schools in Malaysia. The study design is qualitative, using a case study method involving one case across multiple locations. This study involves five Malay language teachers as study participants from five primary schools located in five different states in Malaysia. The research methods used include interviews, observations, and document analysis. The study findings were analyzed thematically using Nvivo14 software. The results of the study identified seven forms of authentic assessment activities implemented by Malay language teachers: role-playing, storytelling, stimulus materials, questioning, edutainment, presentations, and language games. Interview interpretations are supported by observation and document analysis results, which prove that teachers conduct various forms of authentic assessment activities as the best alternative assessments for evaluating primary school students’ Malay language oral skills. Authentic assessment focuses on engaging and realistic activities to facilitate teachers in directly observing students' language development and skills.
Volume: 14
Issue: 1
Page: 682-694
Publish at: 2025-02-01

Identification of the satisfaction of university students through sentiment analysis: a systematic review

10.11591/ijere.v14i1.26640
Omar Freddy Chamorro-Atalaya , Florcita Aldana-Trejo , Nestor Alvarado-Bravo , Constantino Nieves-Barreto , Santiago Aguilar-Loyaga , Carlos Gamarra-Bustillos , Almintor Torres-Quiroz , Alípio Riveros-Cuéllar , Manuel Pérez-Samanamud , Luciano Pérez-Guevara
In these times it is necessary to use tools based on artificial intelligence (AI) that contribute to improving the quality of university education for the benefit of students. This article aims to define the state of the question on the application of sentiment analysis in the identification of student satisfaction, based on the systematic review of scientific publications. The research is of an exploratory level and of a mixed approach. The data collection method was based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) declaration, managing to focus the review based on 27 publications, downloaded from Scopus, ERIC, and Google Scholar. From the systematic review, the following conclusions were reached: the fields of application to a greater extent are found in the academic field and university well-being. Likewise, regarding the contributions achieved, these focused to a greater extent on aspects of the teaching activity, valuing their performance and contributing to their feedback for the redesign of didactic strategies. Finally, in terms of limitations, they focused mainly on the low student participation regarding the use of sentiment analysis to identify student satisfaction; this is due to the lack of regulations or regulations for it is application in the university environment.
Volume: 14
Issue: 1
Page: 37-49
Publish at: 2025-02-01

Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification

10.11591/ijai.v14.i1.pp62-72
Muhlasah Novitasari Mara , Sidiq Syamsul Hidayat , Farika Tono Putri , Dwi Rahmawati , Sri Wahyuni , Muhamad Cahyo Ardi Prabowo , Noer Ni'mat Syamsu Kabir , Ragil Tri Indra
This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Volume: 14
Issue: 1
Page: 62-72
Publish at: 2025-02-01

Multivariate analysis: geography, demographics, and Texas’ post-COVID education

10.11591/ijere.v14i1.30002
Shifang Tang , Zhuoying Wang , Lei Zhang , David D. Jimenez
This study examines the impact of geographic locale on educational outcomes in Texas, focusing on the post-COVID-19 educational landscape. The study evaluates the impact of geographical location on the educational outcomes of eighth-grade students by analyzing STAAR test scores as indicators of academic achievement while adjusting for previous academic results and demographic factors. A sample of 1,145 public school districts across Texas was analyzed, encompassing city, suburban, town, and rural settings. The findings indicate that while geographic locale has a discernible impact on academic achievement, this effect is moderate and intertwined with demographic factors. The research found that rural students unexpectedly outperformed their urban counterpart’s post-pandemic, controlling for their pre-pandemic performance. However, the persistent lower performance in urban districts emphasizes the need to reevaluate educational dynamics. The integration of demographic variables reveals that while they mitigate the influence of geography, they emerge as strong influencers of academic performance on their own. This underscores the need for policies that address the complex interplay between geographic, demographic, and socio-economic factors to narrow educational disparities. The study suggests that targeted interventions are necessary to address the specific needs of different locales, considering the nuanced effects of the pandemic on educational equity.
Volume: 14
Issue: 1
Page: 332-340
Publish at: 2025-02-01
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