Our goal for this article is two-fold: 1) to examine the efficacy of participatory concept mapping as an integration tool for mixed methods research (MMR), and 2) to explore, using concept mapping, pre-service teachers’ epistemic cognition (EC) and its relationship to teaching orientation (TO).  Using a combined developmental and dimensional framework, preservice teachers’ (N=48) concept maps about their (EC) and (TO) were investigated.  Analyses revealed that the majority of the participants were consistent with the EC profiles of either: 1) absolutist, 2) multiplist, or 3) evaluativist.  Participants’ EC and TO were clearly linked and implications for learning, instruction, and teacher education are discussed. Finally, concept mapping was deemed an effective tool for MMR especially as it pertains to integration.

In the current study we examined the relationships between student evaluations of lecturers (teaching surveys) and faculty members' perceptions of these surveys as capable of blocking and limiting their professional advancement. Faculty members are judged and evaluated by academic authorities for their academic performance in research and teaching. 178 questionnaires were collected from the faculty of several academic institutions. We employ a mix method analysis, and form a model that reflects the factors perceived by faculty members as having the potential to block their professional advancement in academia. The research findings show that lecturers are of the opinion that teaching load has a detrimental effect on students' evaluations in the surveys. Lecturers at the beginning of their academic life, those in lower ranks: senior teacher and senior lecturer, address the negative aspects of the surveys more than others. The research findings indicate that although more hours are taught in colleges than at universities, it is harder to receive positive survey ratings at colleges. Moreover, since in Israeli academia research is still the main criterion for promotion – faculty members born in Israel were found to teaching less than those born elsewhere. Hence, faculty members think that student surveys are destructive and entail risks for their professional advancement. Assuming that students' voice and opinions on teaching are important – how can a balance be achieved between the research achievements of faculty members and student satisfaction?

This study examines the effects of the SCAMPER technique-based educational activities in the simple machines unit of a science lesson on students' academic achievement, motivation and attitude. The study examines the effects of the simple machines unit activities in the science lesson through a paired quasi-experimental design, which is one of the quantitative research methods. The sample group of the research consists of 33 eighth-grade students studying in a middle school in the Ortaköy district of the Aksaray province in 2018–2019. The research uses simple random sampling method. The experimental group was given SCAMPER-based activities in the simple machines unit for 4 hours a week with a total of 16 hours, and lessons were conducted with the control group in line with the curriculum. To collect data within the framework of the research, the 'attitude scale towards science lesson', scale for 'students' motivation towards science learning' and 'simple machines unit achievement test' were used. As a result, when compared to the control group, there was a significant difference in the academic achievement and motivation of the experimental group who performed SCAMPER-based activities in the simple machines unit of the science lesson. There was no significant difference between the attitude scores of the experimental and control group as a result of the study.

What are missing in the U.S. education policy of “college for all” are supporting data and indicators on K-16 education pathways, i.e, how well all students get ready and stay on track from kindergarten through college. This study creates synthetic national longitudinal education database that helps track and support students’ educational pathways by combining two nationally-representative U.S. sample datasets: Early Childhood Longitudinal Study- Kindergarten (ECLS-K; Kindergarten through 8th grade) and National Education Longitudinal Study (NELS; 8th grade through age 25). The merge of these national datasets, linked together via statistical matching and imputation techniques, can help bridge the gap between elementary and secondary/postsecondary education data/research silos. Using this synthetic K-16 education longitudinal database, this study applies machine learning data analytics in search of college readiness early indicators among kindergarten students. It shows the utilities and limitations of linking preexisting national datasets to impute education pathways and assess college readiness. It discusses implications for developing more holistic and equitable educational assessment system in support of K-16 education longitudinal database.

Currently, physics education is a science that is still considered abstract by many students and the public. Thus, there is a need for information on the current trends in physics education to adapt to the current situation. Based on the Scopus, the research objective is to explore the ongoing trends in the last ten years and during the COVID-19 pandemic. This research is a bibliometric and bibliometric analysis. The findings show that research related to physics education is dominated by the most developed during the COVID-19 pandemic (2020 – 2021) countries Indonesia. Meanwhile, the Journal of Physics Conference Series is the journal that publishes the most publications (Scopus) related to physics education, followed by the AIP Conference Proceeding. Research implication to research, librarian, and policy maker (1) Research and development need to be carried out in-depth related to the growing trend of physics education so that it can be published in Scopus. (2) Cooperation and collaboration between other universities to increase publications at the international level. (3) The need for continuous research to follow current trends.

The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.

This research aims to prove the influence of teacher efficacy on learning quality with quantitative meta-analysis. The eligibility criteria in this study include: (a) The publication can be searched in Google Scholar, ERIC, DOAJ, Research Gate, and or ScienceDirect; (b) The publication is indexed in Scopus, WoS, SINTA (a portal indexing journal managed by the Ministry of Education and Culture of the Republic of Indonesia, equivalent to DOAJ and Index Copernicus), DOAJ, Index Copernicus, and at least they must be indexed in Google Scholar; (c) The topic of the studies must be relevant; (d) The studies must be carried out in the 2014-2023 year range; (e) The publication must have a value of (r), (t) or (F); (f) The studies have a magnitude of N ≥ 20. This study used the JASP application for data analysis. The results showed that: (a) the 40 studies analyzed were heterogeneous and normally distributed; (b) the influence of teacher efficacy on education quality is classified as strong (p < 0.05; rRE = 0.800); (c) publication bias was not detected. This study concluded that teacher efficacy has a strong influence on education quality.

This study investigated the influence of CThink4CS² Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS² Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills.

The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.

The computing and creative skills of students in Indonesia are still low since the government has not focused on student creativity and computational empowerment programs. This research aims to develop a science, technology, engineering, art, mathematics, and reflection (STEAMER) hybrid learning project model for teachers' creative and computational thinking abilities, as well as analyze elementary school teacher candidates' perceptions of the use of STEAMER hybrid learning model to improve teachers' creative and computational thinking abilities. This research is development research with an analysis, design, development, implementation, and evaluation (ADDIE) model. The instruments used in this study were questionnaires and interviews with experts, lecturers, and elementary school teacher candidates. The research was conducted at eight universities in Indonesia with a total sample of 100 elementary school teacher candidates. Through quantitative and qualitative data analysis, the research results have developed the STEAMER hybrid learning project model based on learning theory, syntax, social systems, support systems, and the instructional impact of learning models. The results of the validation show that the developed learning model is feasible in terms of model, material, media, and language experts. The model is suitable for elementary school teacher education. Furthermore, based on the perceptions of the teacher candidates, it is stated that the STEAMER hybrid learning project can develop the ability of the teacher candidates to think creatively and computationally.

Certain demographics of students may prefer certain modalities, and certain demographics may achieve higher mean grades in some teaching modalities than others. This study used student-section data from five years of all the undergraduate courses at Kennesaw State University (KSU) from 2015 to 2019. This data set with individual student course outcomes included full student demographics and course types, including previous university grade point average (GPA), sex, age, ethnicity, course department, modality, etc. The study only used data from those instructors who taught hybrid sections, as well as in-person and online sections, to avoid the effect of instructor bias. Previous research found that instructors who taught hybrid sections gave higher grades for their online and F2F sections compared to those instructors who had not taught hybrid sections. The results showed that that hybrid-teaching instructors gave higher mean course grades for their hybrid sections than their online or F2F sections and higher mean course grades than non-hybrid teaching instructors in all modalities. This effect held for all demographics.

This pilot study is part of a larger “Decolonization of Digital Learning Spaces” project, which aims to develop research tools for communities that are remote and/or excluded geographically, politically, economically, socially, culturally, and linguistically. The project’s ultimate goal is to work alongside these communities to design their own digital learning tools, networks, and online educational environments by accessing and leveraging their knowledge and skills. Testing the single-criterion card sorting method is the first step toward this goal. Card sorting is an easy, enjoyable, and cost-effective method for data collection and analysis, particularly for researchers working in remote areas with limited access to electricity or the Internet. The pilot explored single-criterion card sorting as a method to elicit knowledge from two diverse cultural and linguistic groups engaged in learning activities within their communities. These groups were from a Deaf and Hard of Hearing (DHH) community in Canada (engaged in a bow-making workshop) and a rural Kabyle community in Algeria (engaged in a traditional cooking lesson). Despite low participant numbers, distinct patterns emerged, indicating the method's effectiveness. The results, though anticipated, were non-random, demonstrating the potential of card sorting in producing patterns indicative of how individuals and/or communities categorize their world(s). Kabyle sortings focused on ingredients, highlighting older individuals as teachers passing along knowledge, while the DHH sortings emphasized face-to-face contact and hand movements in communication. The findings, though modest, established relationships, provided insights into the research context and offered logistical understanding, paving the way for further work with DHH and Kabyle communities towards the design of digital learning spaces.

This article investigates the adoption, simplification, and usage recommendations of the Indonesian Index of Learning Style Short Form (ILS-SF). The aim is to refine the initial Indonesian ILS, compare the suitability between engineering/non-engineering and high school/university, and assess their learning styles. The participants were 678 students (413 females), with an average age of 19.4±1.92 years. The methods used in this study were adopting the existing Indonesian version of ILS, simplifying–reducing the number of items, empirical verification (validity and reliability), and Indonesia data assessment. The results show that the original ILS could be simplified without sacrificing the quality of the model. On the contrary, validity and reliability measures have increased. Confirmatory Factor Analysis (CFA) supports a reduction from 44 to 15 items. It confirms the validity with favorable indices such as CFI (0.972), TLI (0.966), RMSEA (0.021), SRMR (0.049), and GFI (0.999)—Active-Reflective Cronbach's alpha at 0.507, Sensing-Intuitive at 0.590, and Visual-Verbal at 0.553. Indonesian ILS-SF is faster, simpler, more suitable for engineering than non-engineering, and more ideal for undergraduate than high school students. The analysis revealed that sensory (40.2%), active (18%), and visual (10.2%) preferences dominate among Indonesian students. This study highlights assessment tools tailored to diverse educational contexts.

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, examining the challenges and opportunities associated with their adoption. Through a structured selection process, 27 relevant studies published between 2019 and 2023 were analyzed. The results indicate that AI adoption in education remains uneven, with significant barriers such as limited teacher training, technological accessibility gaps, and ethical concerns. However, findings also highlight promising applications, including AI-driven adaptive learning systems, intelligent tutoring, and automated assessment tools that enhance personalized education. The geographical analysis reveals that most research on AI in education originates from North America, Europe, and East Asia, while developing regions remain underrepresented. Without strategic integration, the uneven implementation of AI in education may widen social inequalities, limiting access to innovative learning opportunities for disadvantaged populations. Consequently, this study underscores the urgent need for policies and teacher training programs to ensure equitable AI adoption in education, fostering an inclusive and technologically prepared learning environment.

Artificial Intelligence (AI) is reshaping education across the Asia-Pacific, yet its integration depends on teachers’ readiness and perspectives. This study explores AI adoption among Vietnamese teachers, a critical lens for the region’s digital education reforms, using the Unified Theory of Acceptance and Use of Technology (UTAUT). Through Structural Equation Modeling (SEM) and Latent Dirichlet Allocation (LDA), we analyzed responses from 246 teachers nationwide. Results show attitude strongly predicts adoption intention, with privacy and ethical concerns shaping acceptance, though fears of AI dependence hinder uptake. Uniform challenges across urban-rural and STEM-non-STEM contexts suggest systemic barriers in Vietnam’s education system. Teachers foresee AI as a pedagogical assistant but highlight insufficient training and privacy risks as key obstacles. These findings underscore the need for Asia-Pacific-relevant policies—AI literacy programs, ethical governance, and equitable access—to foster sustainable integration. This research informs regional educational policy by offering a Vietnam-centric model for balancing technological innovation with pedagogical integrity, addressing shared challenges in the Asia-Pacific’s digital transformation.

Analysis of student grades and demographic data to understand the effects of modality and Covid-19 on academic performance is important for universities to understand the impact these factors may have on course grades. This study analyzes all the 615,964 complete undergraduate student-course records from Kennesaw State University (KSU) spanning from 2015 to 2024 to examine the impact of course modality and the Covid-19 pandemic on academic performance. The population dataset includes student demographics (e.g., sex, age, ethnicity), prior GPA, and course characteristics (e.g., department, modality). Descriptive statistics and trend analyses were employed to evaluate grade outcomes across in-person, online, and hybrid modalities over the 9-year period. Results indicate a temporary increase in mean course grades during the Covid-19 period, followed by a return to the pre-pandemic upward trend. Hybrid courses consistently exhibited the highest mean grades throughout the study period. However, consistent patterns across modalities, demographics, and academic units suggest that these factors have limited influence on grade outcomes. These findings raise questions about the reliability of GPA and course grades as indicators of learning success across different instructional contexts and student populations.

This study applies explainable machine learning to identify which vocal attributes in a lecturer’s speech influence students’ views of a lecturer’s charisma, a key contributor to teaching quality. It further explores whether vocal qualities differ between male and female lecturers and how students of different genders respond to these differences, offering insights into voice-related factors that influence the impact of educators. Speech segments from YouTube videos featuring 200 native-English lecturers were evaluated by 900 students using charisma rating scales. A set of attributes related to three primary prosodic dimensions of voice - pitch, rhythm, and loudness - was computed. A random forest classifier was employed to predict the charisma level based on the speech attributes and to list and rank the attributes that contributed most to the prediction. The findings revealed prominent vocal attributes that achieved higher charisma scores in the students' ratings. Same-gender evaluations of charisma were mainly based on pitch, while cross-gender evaluations rely mostly on loudness or rhythm. The automated, interpretable method provides a reliable and efficient way to measure vocal charisma in academic lecturers. It can be adapted to examine additional individual factors that influence the perception of a lecturer’s charismatic presence. It may also be integrated into practice-based tools, designed to support instructors in improving their presentation skills. Our research bridges the fields of applied psychology and computer science to contribute to the development of educational technology.

Within the context of investigating belief systems, this work exemplifies a mixed-method approach. Two approaches are utilized to explore the philosophical, ontological, and epistemological assumptions that university students foster regarding the relationships between knowledge and reality. In the first step, written materials that elaborated on the matter at hand were subjected to content analysis with the assistance of Leximancer, a software that recognizes themes and concepts and turns textual data into concept maps that express networks of meaning. The second step involved conducting a cluster analysis on the data obtained from the questionnaire to identify distinct groups of participants who shared consistent epistemological viewpoints. The results obtained from the two approaches are in agreement and shed light on the prevalent epistemic inclination that favors a constructivist viewpoint. Discussion on the ramifications of the findings, as well as the methodological issues that are pertinent to the present illustration, is provided.