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We, the Publisher, have retracted the following article: A Madhavi et al. (2023). Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model.https://doi.org/10.4108/eetel.4470The article has been retracted due to misconduct during the peer review process. This does not implicate any wrongdoing from the author’s side.We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.

Educational institutions worldwide had to shift the teaching delivery mode from face to face to online teaching during COVID-19. Most of the universities in Vietnam were based on face to face learning until the sudden outbreak of COVID-19. This research study was conducted with 145 respondents and Structural Equation Model (SEM) was used for data analysis. The participants were undergraduate and post-graduate students in public and private universities who studied online during the pandemic in Vietnam. The purpose of this study was to understand what factors have an impact on students’ intentions to study online. The results show that institutional support and perceived enjoyment (satisfaction) affects the students’ intentions to study the course online in the future. Perceived enjoyment (PE) affects the online learning intentions (OLI) and PE is affected by ICT infrastructure and internet speed and access. Hence, this research adds new research variable defined as extrinsic factors (ICT infrastructure and access to the internet), which indirectly influences students’ intentions to learn online. Given the increased use of smart phones with this generation, it is advisable to integrate mobile technology in online learning and QR codes can be one of the ways to integrate that in the course materials. It is further recommended that to increase the perceived enjoyment of the students with the online learning, the lecturers might be encouraged to use videos, audios and instant messaging to contact and provide the feedback to the students. It is important for universities to prepare for any such future crisis. This study results will provide a useful insight to design the online courses effectively by considering all the factors impacting students’ intention and satisfaction.

The effects of school-based ethnic diversity on student well-being and race-related views were examined during the first year in middle school. To capture the dynamic nature of ethnic exposure, diversity was assessed both at the school-level (n = 26) and based on academic course enrollments of African American, Asian, Latino, and White students (n = 4,302; M = 11.33 years). Across all four pan-ethnic groups, school-level ethnic diversity was associated with lower sense of vulnerability (i.e., feeling safer, less victimized, and less lonely) as well as perceptions of teachers' fair and equal treatment of ethnic groups and lower out-group distance. Underscoring the role of individual experiences, exposure to diversity in academic classes moderated the association between school-level diversity and the two aforementioned race-related views.

Many college students in the United States take longer than four years to complete their bachelor’s degrees. Long time-to-degree can increase higher education costs by billions. Time-to-degree can be reduced if students take more credits each term. While academic momentum theory suggests that additional credits may also improve student performance, and there is a strong positive correlation between course load and student performance, high course load may reduce time investment in each course, giving high course load a negative causal effect on performance. Concern about the negative impact of course load on performance, especially for struggling students, may lead to pushback against policies to reduce time-to-degree by increasing course load. Using longitudinal data from a regional four-year university with a high average time-to-degree, we find no evidence that high course loads have a negative impact on student grades, even for students at the low end of the performance distribution. This result is consistent with a model where students substitute time away from non-education activities when their course loads increase.

Although the gender gap in math course-taking and performance has narrowed in recent decades, females continue to be underrepresented in math-intensive fields of Science, Technology, Engineering, and Mathematics (STEM). Career pathways encompass the ability to pursue a career as well as the motivation to employ that ability. Individual differences in cognitive capacity and motivation are also influenced by broader sociocultural factors. After reviewing research from the fields of psychology, sociology, economics, and education over the past 30 years, we summarize six explanations for US women's underrepresentation in math-intensive STEM fields: (a) cognitive ability, (b) relative cognitive strengths, (c) occupational interests or preferences, (d) lifestyle values or work-family balance preferences, (e) fieldspecific ability beliefs, and (f) gender-related stereotypes and biases. We then describe the potential biological and sociocultural explanations for observed gender differences on cognitive and motivational factors and demonstrate the developmental period(s) during which each factor becomes most relevant. We then propose evidence-based recommendations for policy and practice to improve STEM diversity and recommendations for future research directions.

BackgroundA major concern in science education involves the under-representation of many groups in science and technology fields, especially by gender (Brotman and Moore, J Res Sci Teach 45:971–1002, 2008; Clark Blickenstaff, Gend Educ 17:369–386, 2006), stemming from an intersection of systemic obstacles (Cantú, Equity Excell Educ 45:472–487, 2012; Rosa and Mensah, Phys Rev Phys Educ Res 12:020113, 2016). Research on persistence of minoritized populations within science trajectories has often highlighted identity as particularly important (Archer et al., Sci Educ 94:617–639, 2010; Barton and Calabrese, Am Educ Res J 50:37–75, 2007; Barton et al., Am Educ Res J 50:37–75, 2013; Merolla and Serpe, Soc Psychol Educ 16:575–597, 2013).ResultsThis study quantitatively investigated the nature of science identity in over 1300 seventh and ninth grade students from a range of urban US public schools using survey data on science identity, choice preferences, and optional science experiences. Factor analyses validated this conceptualization of science identity as integrating perceived internal and external identity components. Regression analyses revealed the importance of this conceptualization of science identity for driving students’ choices at this crucial developmental period. Furthermore, science identity had a complex differential function in supporting students’ optional science choices by gender.ConclusionsThe novel contribution to the science identity field highlights the specific multi-component ways in which students endorse science identity in middle school and early high school. There was an important finding that science identity has a complex differential function in supporting student’s optional science choices by gender. Thus, at this age, developing a strong science identity is especially critical for girls.

PurposeCourse recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.Design/methodology/approachThe study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.FindingsThe main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.Practical implicationsThe findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.Originality/valueThis study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

This study aims at examining the relationship between entrepreneurship education, culture, and the entrepreneurial intention of college students as well as investigating the moderating role of the entrepreneurial mindset. Structural equation modeling was adopted to gain a detailed understanding of the influence among variables. This study involved approximately 376 university students who enrolled in the entrepreneurship course. The findings indicate that the entrepreneurial mindset has successfully accelerated the entrepreneurial intention of university students. Partially, entrepreneurial culture has an impact on entrepreneurship education and entrepreneurial intention. Additionally, both entrepreneurship education and entrepreneurial culture have a robust correlation with students' entrepreneurial mindset. Contrary to expectations, this study did not find a significant difference between entrepreneurship education and students' entrepreneurial intention. These results imply that the university has positioned itself as a critical intervention in encouraging students' intention through an effective entrepreneurship education model.

As universities prioritise employability, there is increased attention to promoting students’ career decidedness. In this mixed method, cross-sectional study, we explore whether and how students’ interest in their academic subject affects their career decidedness. Using surveys of 428 undergraduates studying sciences in a UK university (60% F, average age = 19.9) and case examples from follow-up interviews with 15 students, we examine students’ interest development and its relation to their career decidedness and their desire for meaningful, interesting work. Findings showed that most students who were studying science in university had a well-developed interest that had motivated their choice of programme, and their subject interest and career decidedness were linked. Regression analyses indicated that students’ interest in their subject was a significant predictor of career decidedness, mediated by students’ desire to pursue that interest in their career. Open-ended comments on the surveys suggested that decidedness was informed by coursework, proactive career exploration, work experience, interest, feasibility, and familial contacts. Interviews confirmed these factors and illustrated how they contributed to students’ career decidedness. We propose implications for academics and career counsellors who might help students refine their interest by considering connections between their academic subject, interest, and related career options. We argue that educators and policy-makers need to reframe employability interventions and think beyond teaching students skills or attributes that lead to employment. Educators can start with students’ interest in their subject and support students’ exploration of how they can continue to pursue that interest in various careers.

Aim/Purpose: This systematic literature review investigates the underlying factors that influence the gap between the popularity of online learning and its completion rate. The review scope within this paper includes an observation of possible causal aspects within the non-ideal completion rates in online learning environments and an identification of recommended strategies to increase retention rates. Background: While online learning is increasingly popular, and the number of online students is steadily growing, student retention rates are significantly lower than those in the traditional environment. Despite the multitude of studies, many institutions are still searching for solutions for this matter. Methodology: A systematic literature review was conducted on 40 studies published between 2010 and 2018. We established a set of criteria to guide the selection of eligible articles including topic relevance (aligned with the research questions), empirical studies, and publication time frame. Further steps were performed through a major database searching, abstract screening, full-text analysis, and synthesis process. Contribution: This study adds to expanding literature regarding student retention and strategies in online learning environments within the higher education setting. Findings: Revealed factors include institutional support, the level difficulty of the programs, promotion of a sense of belonging, facilitation of learning, course design, student behavioral characteristics, and demographic variables along with other personal variables. The recommended strategies identified for improving student retention are early interventions, at-all-times supports for students, effective communication, support for faculty teaching online classes, high-quality instructional feedback and strategies, guidance to foster positive behavioral characteristics, and collaboration among stakeholders to support online students. Recommendations for Practitioners: Since factors within the open systems of online learning are interrelated, we recommend a collective effort from multiple stakeholders when addressing retention issues in online learning. Recommendation for Researchers: We recommend that fellow scholars consider focusing on each influential factor and recommendation in regard to student retention in online learning environments as synthesized in this study. Findings will further enrich the literature on student retention in online learning environments. Future Research: Future research may investigate various data-mining and analytics techniques pertaining to detection and prediction of at-risk students, the efficacy of student support and faculty support programs, and ways to encourage struggling students to adopt effective strategies that potentially engender positive learning behaviors.