Three Types of Interaction

The rapid expansion of the Information and Communication Technologies (ICTs) has transformed learners into digital learners, requiring teachers to integrate technology into their pedagogical approaches, where teachers’ attitudes, technological knowledge, and skills play a significant role in its effective integration. From this perspective, the current study presents teachers’ perceptions regarding technology integration in their teaching-learning practices at all educational levels in light of the previous studies performed in the last 5 years in Pakistan. The findings reveal that teachers exhibit positive perceptions regarding technology integration in teaching-learning practices. They believe that technology-incorporated teaching assists them in enhancing their instructional practices effectively, making the learning process exciting and interactive, and keeping learners motivated. Regarding barriers, the slow speed of the internet, load shedding, lack of infrastructure, online teaching experience, and training were reported as the main obstacles that hinder teachers from effective integration of ICT into their teaching practices. Accordingly, the study findings suggest that concerned authorities should set clear and effective policies to make efficient use of ICT by allocating a sufficient budget and ensuring all necessary facilitation (e.g., ICT infrastructure, tools, software, internet, and labs) in all educational institutions. Furthermore, particular attention should be devoted to supplying adequate opportunities for the career development of teachers in developing technological competencies, which help them successfully use ICT in their instructional practices.

/pdf/teachers-perceptions-of-technology-integration-in-teaching-34lif3j2.pdf

Teachers’ Perceptions of Technology Integration in Teaching-Learning Practices: A Systematic Review

Green travel mobility of dockless bike-sharing based on trip data in big cities: A spatial network analysis

The outbreak of COVID-19 has caused significant disruption in all sectors and industries around the world. To tackle the spread of the novel coronavirus, the learning process and the modes of delivery had to be altered. Most courses are delivered traditionally with face-to-face or a blended approach through online learning platforms. In addition, researchers and educational specialists around the globe always had a keen interest in predicting a student’s performance based on the student’s information such as previous exam results obtained and experiences. With the upsurge in using online learning platforms, predicting the student’s performance by including their interactions such as discussion forums could be integrated to create a predictive model. The aims of the research are to provide a predictive model to forecast students’ performance (grade/engagement) and to analyse the effect of online learning platform’s features. The model created in this study made use of machine learning techniques to predict the final grade and engagement level of a learner. The quantitative approach for student’s data analysis and processing proved that the Random Forest classifier outperformed the others. An accuracy of 85% and 83% were recorded for grade and engagement prediction respectively with attributes related to student profile and interaction on a learning platform. 

/pdf/predictive-modelling-and-analytics-of-students-grades-using-1a5zpkpq.pdf

Predictive modelling and analytics of students’ grades using machine learning algorithms

PurposeThis study investigates how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' purchasing and gift-giving behaviors and how broadcaster popularity moderates social interaction-oriented content's effect on the two different behaviors in live-streaming commerce.Design/methodology/approachA research model was proposed and empirically tested using a panel data set collected from 537 live streams via Douyin (the Chinese version of TikTok), one of the most popular live broadcast platforms in China. A fixed-effects negative binomial regression model was used to examine the proposed research model.FindingsThis study's results show that social interaction-oriented content in broadcasters' live speech has an inverted U-shaped relationship with broadcast viewers' purchasing behavior and shares a positive linear relationship with viewers' gift-giving behavior. Furthermore, broadcaster popularity significantly moderates the effect of social interaction-oriented content on viewers' purchasing and gift-giving behaviors.Originality/valueThis research enriches the literature on live-streaming commerce by investigating how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' product-purchasing and gift-giving behaviors from the perspective of broadcast viewers' attention. Moreover, this study provides some practical guidelines for developing live speech content in the live-streaming commerce context.

https://www.emerald.com/insight/content/doi/10.1108/INTR-11-2021-0861/full/pdf?title=can-social-interaction-oriented-content-trigger-viewers-purchasing-and-gift-giving-behaviors-evidence-from-live-streaming-commerce

Can social interaction-oriented content trigger viewers' purchasing and gift-giving behaviors? Evidence from live-streaming commerce

In the field of learning analytics, mining the regularities of social interaction and cognitive processing have drawn increasing attention. Nevertheless, in MOOCs, there is a lack of investigations...

Exploring the Relationship Between Social Interaction, Cognitive Processing and Learning Achievements in a MOOC Discussion Forum:

Analyzing and mining students’ behaviors and interactions from big data is an essential part of education data mining. Based on the data of campus smart cards, which include not only static demographic information but also dynamic behavioral data from more than 30000 anonymous students, in this paper, the evolution features of friendship and the relations between behavior characters and student interactions are investigated. On the one hand, four different evolving friendship networks are constructed by means of the friend ties proposed in this paper, which are extracted from monthly consumption records. In addition, the features of the giant connected components (GCCs) of friendship networks are analyzed via social network analysis (SNA) and percolation theory. On the other hand, two high-level behavior characters, orderliness and diligence, are adopted to analyze their associations with student interactions. Our experiment/empirical results indicate that the sizes of friendship networks have declined with time growth and both the small-world effect and power-law degree distribution are found in friendship networks. Second, the results of the assortativity coefficient of both orderliness and diligence verify that there are strong peer effects among students. Finally, the percolation analysis of orderliness on friendship networks shows that a phase transition exists, which is enlightening in that swarm intelligence can be realized by intervening the key students near the transition point.

pdf/evolution-features-and-behavior-characters-of-friendship-1y5tswoweh.pdf

Evolution features and behavior characters of friendship networks on campus life

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Exploring the Relationship Between Social Interaction, Cognitive Processing and Learning Achievements in a MOOC Discussion Forum:

Evolution features and behavior characters of friendship networks on campus life