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Article
Publication date: 31 January 2025

Jishan Zhu and Michael Onorato

The paper aims to compare remote teaching effectiveness with face-to-face classroom teaching. This study seeks to investigate the influence of remote teaching on student academic…

2

Abstract

Purpose

The paper aims to compare remote teaching effectiveness with face-to-face classroom teaching. This study seeks to investigate the influence of remote teaching on student academic learning during the pandemic compared to prepandemic, utilizing self-reported data from students.

Design/methodology/approach

The analysis is based on information extracted from the Student Course Evaluation. The study defines Spring 2019 and Fall 2019 semesters as prepandemic and labels them as the comparison base period. Spring 2020, Fall 2020 and Spring 2021 semesters are considered “during the pandemic” for comparison.

Findings

Upon comparing the average scores, the study findings indicate that students experienced a reduction in learning during the pandemic in contrast to the prepandemic period. Notably, a substantial decline in student academic learning was observed in the Spring 2020, followed by the Fall 2020 semesters. However, with the passage of time, the adverse impact of remote teaching on student learning showed a gradual decrease. In the Spring 2021 semester, students’ self-reports on learning in their business courses returned to prepandemic levels.

Research limitations/implications

The study has relied on secondary data sources and student self-reporting. Future research should aim to use more controlled data to mitigate the limitations associated with secondary data.

Practical implications

The study suggests that remote teaching could be as effective as face-to-face classroom teaching, and administrators of universities and colleges may consider it a viable option in the future.

Originality/value

This paper adds an important (probably first) empirical study of remote teaching effectiveness to the field of online learning literature.

Details

SAM Advanced Management Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2996-6078

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Article
Publication date: 9 March 2023

Hang Yin, Jishan Hou, Chengju Gong and Chen Xu

The behavior of the entities in a small and medium-sized enterprise (SME) cooperation network is influenced by the core enterprise. Addressing the problem of how the network…

157

Abstract

Purpose

The behavior of the entities in a small and medium-sized enterprise (SME) cooperation network is influenced by the core enterprise. Addressing the problem of how the network vulnerability changes when the core enterprise is attacked is a challenging topic. The purpose of this paper is to reveal the failure process of SME cooperation networks caused by the failure of the core SME from the perspective of cascading failure.

Design/methodology/approach

According to the Torch High Technology Industry Development Center, Ministry of Science & Technology in China, 296 SMEs in Jiangsu province were used to construct an SME cooperation network of technology-based SMEs and an under-loading cascading failure model. The weight-based attack strategy was selected to mimic a deliberate node attack and was used to analyze the vulnerability of the SME cooperation network.

Findings

Some important conclusions are obtained from the simulation analysis: (1) The minimum boundary of node enterprises has a negative relationship with networks' invulnerability, while the breakdown probability has an inverted-U relationship with networks' invulnerability. (2) The combined effect of minimum boundary and breakdown probability indicates that the vulnerability of networks is mainly determined by the breakdown probability; while, minimum boundary helps prevent cascading failure occur. Furthermore, according to the case study, adapting capital needs and resilience in the cooperation network is the core problem in improving the robustness of SME cooperation networks.

Originality/value

This research proposed an under-loading cascading failure model to investigate the under-loading failure process caused by the shortage of resources when the core enterprise fails or withdraws from the SME cooperation network. Two key parameters in the proposed model—minimum capacity and breakdown probability—could serve as a guide for research on the vulnerability of SME cooperation networks. Additionally, practical meanings for each parameter in the proposed model are given, to suggest novel insights regarding network protection to facilitate the robustness and vulnerability in real SME cooperation networks.

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Article
Publication date: 28 February 2019

Gabrijela Dimic, Dejan Rancic, Nemanja Macek, Petar Spalevic and Vida Drasute

This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment.

220

Abstract

Purpose

This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment.

Design/methodology/approach

To extract the most relevant activity feature subset, different feature-selection methods were applied. For different cardinality subsets, classification models were used in the comparison.

Findings

Experimental evaluation oppose the hypothesis that feature vector dimensionality reduction leads to prediction accuracy increasing.

Research limitations/implications

Improving prediction accuracy in a described learning environment was based on applying synthetic minority oversampling technique, which had affected results on correlation-based feature-selection method.

Originality/value

The major contribution of the research is the proposed methodology for selecting the optimal low-cardinal subset of students’ activities and significant prediction accuracy improvement in a blended learning environment.

Details

Information Discovery and Delivery, vol. 47 no. 2
Type: Research Article
ISSN: 2398-6247

Keywords

Available. Open Access. Open Access
Article
Publication date: 9 April 2020

Xiaodong Zhang, Ping Li, Xiaoning Ma and Yanjun Liu

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was…

973

Abstract

Purpose

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Design/methodology/approach

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

Findings

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Practical implications

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

Originality/value

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

Details

Smart and Resilient Transportation, vol. 2 no. 1
Type: Research Article
ISSN: 2632-0487

Keywords

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