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Matteo Opizzi, Michela Loi and Orsola Macis
Doctoral students are promising entrepreneurial actors in university-based ventures, which positively impact the external environment and create value for their universities. In…
Abstract
Purpose
Doctoral students are promising entrepreneurial actors in university-based ventures, which positively impact the external environment and create value for their universities. In this article, the authors extend current research on academic entrepreneurship by shedding light on the role of university support in the early stage of Ph.D. entrepreneurship. Based on social information processing theory, the authors posit that academic entrepreneurship results from the interplay between doctoral students' human capital and university-level support. A multilevel model is proposed and empirically tested to shed light on the cradle of doctoral students' entrepreneurship by explaining the variance of their entrepreneurial alertness and intentions.
Design/methodology/approach
A model is proposed that explains the combined effect of specific human capital and different forms of university support on doctoral students' cognitive transition from entrepreneurial alertness to intentions. The model was then tested through structural equation modeling (SEM) and multigroup analysis (MGA) on a sample of 187 doctoral students enrolled in Italian universities.
Findings
The SEM results reveal that doctoral students' entrepreneurial alertness is influenced by perceived educational support and human capital. The MGA demonstrates that those who perceive a higher level of support for concept and business development from universities are more likely to convert their alertness into intentions than those who perceive lower support.
Originality/value
The present paper brings to the stage doctoral students as an extremely promising entrepreneurial target. In doing so, it extends academic entrepreneurship studies by detailing how and when the different forms of university support influence their entrepreneurial decisions, along with individual dimensions.
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Huaxiang Song, Chai Wei and Zhou Yong
The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of…
Abstract
Purpose
The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.
Design/methodology/approach
This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.
Findings
This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.
Originality/value
This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.
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Juan P. Perez, Izaias Martins, Maria Dolores Mahauad and Paul Oswaldo Sarango-Lalangui
The purpose of this study is to test the effect of entrepreneurship education programs (EEPs) on the individual entrepreneurial orientation (IEO) by considering the…
Abstract
Purpose
The purpose of this study is to test the effect of entrepreneurship education programs (EEPs) on the individual entrepreneurial orientation (IEO) by considering the innovativeness, proactiveness and risk-taking dimensions of undergraduate students and the role of these dimensions on the relationship between program inspiration (PI) and entrepreneurial intention (EI).
Design/methodology/approach
This study uses a pre- and post-test analysis with data from 1,423 undergraduate students from two emerging countries in Latin America: Colombia and Ecuador. To verify the effect of the EEP on innovativeness, proactiveness and risk-taking at two-time points, the analysis of median for the difference of two paired populations (Sign Test and the Wilcoxon signed-rank test) was used. In addition, structural equation modeling analysis under the covariance and multigroup approach was applied to test the relationship between PI, IEO and EI.
Findings
The results of this study show that the EEP enhances innovativeness, proactiveness and risk-taking whose effects are more prominent in Ecuadorian students. Equally importantly, the EEP benefits represented in PI translate into higher EI through the mediating effect of the IEO dimensions for both the Colombian and Ecuadorian contexts, where the findings suggest no significant differences between two groups.
Originality/value
The findings of this study contribute to a better understanding of entrepreneurship education's role in entrepreneurial competence's development and intention by revealing the role of IEO dimensions. This study is one of the first studies that applies the IEO to contribute to the literature on the relationship between entrepreneurship education and intention from an emotional entrepreneurial perspective.
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Pallavi Srivastava, Trishna Sehgal, Ritika Jain, Puneet Kaur and Anushree Luukela-Tandon
The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with…
Abstract
Purpose
The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.
Design/methodology/approach
Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.
Findings
Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.
Originality/value
The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.
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Laura Hedin, Lydia Gerzel-Short, Lisa Liberty and Jason Pope
District-university partners increasingly rely on “grow-your-own” licensure programs to address teacher shortages. Because vacancies in special education represent a chronic…
Abstract
Purpose
District-university partners increasingly rely on “grow-your-own” licensure programs to address teacher shortages. Because vacancies in special education represent a chronic issue, our district-university partnership developed LEAP – the Licensed Educators’ Accelerated Pathway, successfully preparing 26 paraprofessionals as special education teachers (SEs). We describe a model university-district partnership in which we collaborated to design and implement paraprofessionals’ SE licensure program.
Design/methodology/approach
In this general review, we describe a district-university partnership collaboration that resolved barriers experienced by paraprofessionals working toward licensure in special education (Essential #4, Reflection and Innovation). The specialized design and partnership solutions were grounded in SE preparation research literature.
Findings
25 (28 entered the program and 25 completed) paraprofessionals from one large urban and several regional districts completed special education licensure through LEAP. Slightly more than half of LEAP participants were Black or Hispanic (see Table 1), contributing to the diversification of SE workforce. University-district partnership was successful in designing and delivering a program that allowed participants: a) to remain employed, b) attend evening classes in their geographic region or online, c) complete all field experiences in sponsoring districts (Essential #2) and d) receive concierge advising from a “completion coach.” We describe solutions to barriers experienced by paraprofessionals and advocate for district-university collaboration to address chronic teacher shortages.
Research limitations/implications
Limitations include lack of data on success of program completers during their first year of teaching as they began this work in Fall 2023. Further, because the participating district was large and urban, generalization of program details for small and rural districts is difficult.
Practical implications
Practical tips for developing grow-your-own special education licensure programs are providing. Detailed descriptions of barriers candidates experienced and ways the district-university partners resolved these issues are included. Programs like the one described has the potential to positively impact teacher pipeline issues.
Social implications
The program described provided highly-trained teachers to fill chronic vacancies in special education in three participating districts/agencies. Because students receiving special education services are at risk for school failure and are disproportionately impacted by teacher turnover, addressing this area through grow-your-own licensure programs represents a diversity, equity and inclusion initiative. Further, upskilling diverse paraprofessionals to licensed teacher roles represent an economic boost, which they might not otherwise have achieved.
Originality/value
Available research literature signals alarm over persistent teacher shortages in hard-to-staff districts and lack of diversity in the teacher workforce, but few published accounts describe successful programs. Partner collaboration fostered a re-imagining of course formatting and delivery to accommodate adult learners, avoiding problems often reported with alternative programs.
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Kasun Gomis, Mandeep Saini, Mohammed Arif and Chaminda Pathirage
Lack of appropriate student support and drawbacks in academic progression signify the importance of enhancing assessment and feedback in higher education (HE). Although assessment…
Abstract
Purpose
Lack of appropriate student support and drawbacks in academic progression signify the importance of enhancing assessment and feedback in higher education (HE). Although assessment and feedback are significant in HE, minimal empirical research holistically explores the best practices. This study aims to address the niche and develop a decisive guideline for enhancing assessment setting and feedback provision within HE curricula.
Design/methodology/approach
A systematic approach was taken to obtain data for the study: a literature review underpinning the thematic content analysis of study documents, followed by semi-structured interviews. Document analysis contained mid-module reviews/student feedback; rubrics used in assessment; and formative/summative feedback provided for the graded work. Documental analysis informed the key attributes of the semi-structured interview. Interpretive structural modelling (ISM) analysis identified the influence and reliance of each driver.
Findings
This study revealed 15 drivers – 4 fundamental, 6 significant and 5 important – for enhancing assessment and feedback. The level partitioning from the ISM analysis established that all assessment and feedback needs to be underpinned by the university policy and fed into the assessment regime and marking scheme. This study identified that National Student Survey results were significantly improved due to implementing said drivers compared with the national and sector benchmarks.
Practical implications
The developed drivers enable the best practices in assessment setting and feedback provision. The level partition diagram can be used as a decisive guideline or a provisional framework in assessment and feedback provision for quality assurance in HE.
Originality/value
This study is one of, if not the only, to develop a guideline for signposting drivers and their influence and reliance to enhance assessment and feedback in a holistic HE setting. The developed drivers and the level partition diagram bring novelty and add to the current body of knowledge.
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