Search results

1 – 3 of 3
Open Access
Article
Publication date: 1 December 2015

Yanhui Han*, Shunping Wei and Shaogang Zhang

In the field of education in China, a large number of learning management systems have been deployed, in which vast amounts of data on learners and learning processes have been…

5584

Abstract

In the field of education in China, a large number of learning management systems have been deployed, in which vast amounts of data on learners and learning processes have been stored. How can one make use of these data? How can one transform the data into information and knowledge that inform decision-making in teaching and optimize learning? These questions have become a matter of concern for educators and learners. Learning analytics helps to unlock the value of the learning process data, so that the data can become an important basis for prudent decisions and process optimization. 'Learning analytics' was listed in the 2013 NMC Horizon Report as one of the emerging technologies that will have a great impact on learning, teaching and innovative research in higher education in two to three years. The report notes that learning analytics aims to decipher trends and patterns in the teaching and learning process from educational big data. In this paper, an online course on the Moodle platform is used for the research. The study examines reflection on online teaching and learning based on massive records of the learning process from the perspective of a tutor employing learning analytics. It is a brand new form of reflection on teaching and learning. The analysis of interactive course forums can help tutors to focus on key teaching and learning activities, and achieve more accurate analysis than with conventional face-to-face teaching activities. The research indicates that learning analytics is effective in supporting tutor reflection on interactive online teaching and learning.

Details

Asian Association of Open Universities Journal, vol. 10 no. 2
Type: Research Article
ISSN: 1858-3431

Keywords

Article
Publication date: 3 April 2020

Chengjing You

This paper aims to convict the offender of real concurrence offenses of the most severe offense and applying the most severe penalty will result in no distinction between the…

Abstract

Purpose

This paper aims to convict the offender of real concurrence offenses of the most severe offense and applying the most severe penalty will result in no distinction between the perpetrator who conducted more than one act and the one who conducted only one act. This approach deviates from the purpose of criminal law. The real concurrence of offenses means several offenses, the perpetrator’s dangerousness and culpability are much higher than the perpetrator who commits just one crime, so combined punishments for several offenses should be applied to the real concurrence of offenses.

Design/methodology/approach

If the depositors are acquaintances or relatives and friends, the relationship can be explained by “personality trust.” If the depositors are strangers, but they have complied with their duties of care, the deposit relationship can be explained by “system trust.”

Findings

The real concurrence of offenses means several offenses, the perpetrator’s dangerousness and culpability are much higher than the perpetrator who commits just one crime, so combined punishments for several offenses should be applied to the real concurrence of offenses.

Originality/value

The principle of choosing the most severe punishment applied to the real concurrence of offense should be abolished. As the perpetrator separately conducts two acts at different times, these acts infringe on different legal interests. Although these acts exist closely, the authors cannot deny that these acts constitute more than one offense.

Article
Publication date: 5 April 2024

Liyi Zhang, Mingyue Fu, Teng Fei, Ming K. Lim and Ming-Lang Tseng

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

Abstract

Purpose

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

Design/methodology/approach

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

Findings

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

Originality/value

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

Keywords

1 – 3 of 3