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.

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 analyzes the effects of restricting face-to-face classes during the lockdown and students' preparation for face-to-face instruction. During the academic year 2021-2022 break, it was conducted at Nueva Ecija University of Science and Technology (NEUST)-Gabaldon Campus's College of Education. This study employed a descriptive correlational and descriptive comparative research design. The 151 education students who participated in this study were chosen using a stratified sampling method. According to the study, students received satisfactory to very satisfactory grades during the lockdown. The study also showed that after the lockdown and after the Commission on Higher Education recommended face-to-face classes, the majority of respondents agree that they are academically, socio-emotionally, and physically prepared to go through a face-to-face mode of learning. The majority of them prefer face-to-face classes to any other form of distance learning. The general weighted average of a student is a predictor of academic readiness in face-to-face classes. In addition, students' general weighted averages have direct link to their socio-emotional readiness. Students' profiles, on the other hand, have no impact on their physical readiness. There is no significant difference in student preparation in face-to-face classes when students are grouped by gender, year and section, and civil status. There is no association between the student profile and their preferred mode of learning. The theoretical and practical ramifications of the research were also addressed.

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.