Eyal Eckhaus

The growing availability of digital learning resources has reduced the necessity of physical campus attendance, raising questions about the relationship between learning preferences and attendance behavior. This study examined whether online learning orientation and reduced necessity for physical presence are associated with lower attendance or coexist with continued campus participation. Survey data from 1001 university students in Israel were analyzed using exploratory and confirmatory factor analyses and structural equation modeling, including a split-sample robustness assessment. The analyses supported a four-factor structure, with preferences for online learning and perceived value of campus-based learning loading in opposite directions on a common latent dimension. Contrary to a substitution perspective, stronger online learning orientation and reduced necessity for physical attendance were positively associated with self-reported campus attendance. Campus social value was associated with a stronger campus-based orientation but, contrary to expectations, with lower attendance. Campus environment and services showed a positive conditional association with attendance but no significant association with learning modality orientation. The structural model explained 31.5% of the variance in attendance. These findings highlight the distinction between learning preferences, perceived necessity, and attendance behavior, suggesting that digital flexibility does not necessarily correspond to reduced physical participation.

Background: This exploratory study investigates whether STEM (science, technology, engineering, and mathematics) students’ increasing reliance on off-campus resources (e.g., online platforms, private tutors) reflects an authentic preference for autonomous learning or a compensatory response to perceived deficiencies in on-campus instruction. Methodology: Using a mixed-methods design, data were collected from 118 engineering and science students. A model was developed to examine the relationship between the intensity of student criticism and their declared preference for off-campus learning. Findings: The model revealed a significant negative relationship between the intensity of criticism and the preference for off-campus instruction. This suggests that for highly critical students, external resources function primarily as a compensatory mechanism for “needs frustration” rather than a preferred alternative. The results imply that these students continue to value the frontal model but find its current implementation insufficient to meet their pedagogical needs. Conclusion: These findings challenge the assumption that digital trends signify a voluntary abandonment of the classroom. Instead, reliance on external resources is positioned as a reactive, compensatory strategy. Higher education institutions should prioritize revitalizing frontal instruction through enhanced clarity and focus to reduce dependency on off-campus platforms and restore the value of the campus experience.

Background: This study explores an innovative integration of big data analytics techniques aimed at enhancing predictive modeling in financial markets. It investigates how combining sentiment analysis with latent profile analysis (LPA) can accurately forecast stock prices. This research aligns with big data methodologies by leveraging automated content analysis and segmentation algorithms to address real-world challenges in data-driven decision-making. This study leverages advanced computational methods to process and segment large-scale unstructured data, demonstrating scalability in data-rich environments. Methods: We compiled a corpus of 3843 financial news articles on Teva Pharmaceuticals from Bloomberg and Reuters. Sentiment scores were generated using the VADER tool, and LPA was applied to identify eight distinct sentiment profiles. These profiles were then used in segmented regression models and Structural Equation Modeling (SEM) to assess their predictive value for stock price fluctuations. Results: Six of the eight latent profiles demonstrated significantly higher predictive accuracy compared to traditional sentiment-based models. The combined profile-based regression model explained 47% of the stock price variance (R² = 0.47), compared to 10% (R² = 0.10) in the baseline model using sentiment analysis alone. Conclusion: This study pioneers the use of latent profile analysis (LPA) in sentiment analysis for stock price prediction, offering a novel integration of clustering and financial forecasting. By uncovering complex, non-linear links between market sentiment and stock movements, it addresses a key gap in the literature and establishes a powerful foundation for advancing sentiment-based financial models.

This study explores students’ perceptions of the advantages of academic campuses as the location of studies that involve social interaction, with a focus on the role of social interaction in the learning experience. The study examines students’ perspectives approximately five years after the time of COVID-19, when online studies have become more prevalent. Participants were 1048 students from several departments at two academic institutions, a university and a college, of whom 39.1% (407) were male and 60.9% (633) female. The age of the respondents ranged from 18–23 (30.4%), 24–30 (60.3%), and 31–63 (9.3%). Among all respondents, 64.2% were studying social sciences and 35.8% engineering. A mixed methods research design was employed, combining qualitative and quantitative analysis. Structural equation modeling (SEM) was utilized to test the goodness-of-fit of the presented model. The research findings showed that measures of comprehensibility, concentration, and contact with the lecturer enhance learning in a physical classroom more than learning via online teaching. However, interaction with peers does not improve one’s studies at all and is not a sufficient reason to attend classes. These findings are based on analysis of survey responses, after applying SEM to test the goodness-of-fit of the presented model. The final model showed a good fit: CMIN/DF = 1.26, CFI = 0.999, NFI = 0.996, TLI = 0.994, RMSEA = 0.02. The findings of this study may hold importance for leaders of higher education when endeavoring to plan teaching, learning, and evaluation at academic institutions and for shaping the academic campus as a significant educational space in the future. Moreover, the findings may have important implications for education management strategies towards sustainable development. Higher education institutions need to re-evaluate the role of the physical campus and social interaction within it in the era of remote learning.

It has been five years since the COVID-19 pandemic began in 2019, forcing universities to hastily adopt online teaching. This study focuses on two under-researched topics: whether the shift online may change universities’ model to not require physical attendance and how students’ socioeconomic status impacts their preferences around online versus in-person learning. The research hypotheses suggest that undergoing an online teaching experience positively influences preferences for online teaching, which in turn impacts the desire for institutional changes toward an online environment. Based on 1854 questionnaires completed at several academic institutions, we employed a mixed methods design. We performed Exploratory Factor Analysis (EFA) followed by Structural Equation Modeling (SEM) to test the models’ goodness of fit and presented the Online Teaching Preference and Socioeconomic Effect (OTPSE) model. The findings show that students who felt their teaching improved through online classes preferred this format and chose universities without mandatory on-campus classes. Additionally, students from higher/middle socioeconomic backgrounds exhibited a greater preference for online learning than those from lower socioeconomic backgrounds. Overall, the results suggest that higher education leaders need thorough discussions on the topic. These findings may be valuable for education managers and policymakers when considering the implementation of sustainable education strategies, including the integration of online teaching methods. By understanding students’ preferences and the factors that influence their choices, education managers can develop effective strategies to promote sustainable development in education systems.

Much has been written about myths and facts concerning teaching surveys completed by students at academic institutions. The myths usually emerge among faculty members based on their subjective point of view, personal experience, or intuitive interpretation of events. The following case study is the first of its kind, and it follows how academic faculty members evaluate performance measurement of academic teaching and teaching surveys in particular, while examining the change that has occurred over the last decade. The case analysis, examining instructors’ evaluation of teaching surveys completed by students, is based on a group of senior faculty members in Israeli academic institutions. The current study is unique in examining how academic faculty members perceive alternative options for measuring their overall performance in academic teaching as manifested in teaching surveys. One hundred eighty-two questionnaires were collected from senior faculty members at academic institutions, comprised of open-ended questions concerning suggestions for alternative teaching evaluation surveys and their structure. The research findings show that the instructors mention “professional” alternatives and perceive teaching surveys as an unprofessional and populist tool. Assuming that students’ voices and their opinion of teaching are important, professional alternatives for evaluating and improving teaching should find expression—and instructors relate significantly to professional elements at academic institutions as potentially helpful factors.

BACKGROUND: Over the years, persons with disabilities have suffered unjustifiably due to a low employment rate, largely the result of unsupportive and negative attitude from employers. Such attitudes are exacerbated during periods of economic stress, with a case par excellence being the current COVID-19 pandemic. Prior studies have addressed common concerns of employers within the workplace. However, the dimensions of these concerns extend beyond the internal working environment, to the outward-facing work environment, involving interactions with customers and suppliers. These concerns have not, thus far, been properly addressed. OBJECTIVE: To develop the Service from People With Visual Impairment (SPVI) psychometric measure, a 6-item employers’ attitude assessment tool. METHODS: This study is based on 1,036 questionnaires collected using Online Panel Data (OPD), from managers who have hiring authority. We performed Exploratory Factor Analysis (EFA) followed by Confirmatory Factor Analysis (CFA) for discriminant and convergent validity. Finally, we present an empirical model comprising a stable single factor and establish predictive validity using Structural Equation Modeling (SEM). RESULTS: CFA showed good fit to the observed data; CMIN/DF = 1.94, p > 0.05, CFI = 1, TLI = 0.99, NFI = 1, RMSEA = 0.03. SEM showed good fit; CMIN/DF = 1.91, CFI = 1, TLI = 0.99, NFI = 0.99, RMSEA = 0.03. CONCLUSION: The results suggest that the instrument could become a pragmatic tool to assess employer attitudes to employ people with visual impairment or blindness due to external work environment concerns. The tool is relevant to a wide range of circumstances, including economic downturns. Practical considerations are discussed.