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Data from social learning development, during COVID-19 how international students use media platforms as crowdsource technology to solve learning difficulties

Citation

Muhideen, Sayibu (2020), Data from social learning development, during COVID-19 how international students use media platforms as crowdsource technology to solve learning difficulties, Dryad, Dataset, https://doi.org/10.5061/dryad.nk98sf7rz

Abstract

This study lens cannot detach mobile-learning communication from efficient broadband, during social distance protocol, for continuous learning. The study aims to self-actualized knowledge-sourcing among individuals in an interconnected cluster of multi-community platforms. Crowdsource is characterized by a communication science perspective using mobile-learning in groups and/or clusters. Integrated TPB and Bandura’s Social Learning Theory (SLT) induced and investigated 361 respondents, stratified of students, teachers, and researchers during the COVID-19. The IBM Amos v. 25 for the analysis found R² = 0.92 and (65.4, 34.6) for male and female demographic respectively. Results found significant direct and indirect Attitude, Self-learner usefulness, and crowdsource were positive to actual performance. Where broadband moderated on mobile learning behavior run-up significantly. Mobile learning mediation gives in magnificent interactive operation. Generally, crowdsource at the individual level enhanced collaborated problem-solving tasks pro-COVID-19. An Individual's sourcing knowledge is a creative task and timely routine learning in any critical period. Results suggested mobility of learning makes a mountain of molehills in knowledge interactivity among learners in groups. Therefore, encourages the clustering of network learning, easing learning, through effective broadband social intervention phenomena.

Methods

Participants

The study population of students and teachers by induction of the SEM among foreigners in China, the participants were a cluster of foreigners in Mainland China. Due to the pandemic, they were lockdown however, needed to exchange knowledge from similar of different course areas, they multistage stratified into group platforms, pro-COVID-19 interconnected social learning based on different university campuses. Campus e-platforms were department and/or course-based, each group discussed and chatrooms network via mobile social learning exchanges of knowledge. Participants were connected via mobile learning as communication exception, therefore, mobile communication in the survey, selected social media platforms like; Tencent, WeChat, QQ, and Sino Weibo for the response due to commonly used in China. Using the stratified sampling technique to and fro students, teachers, and researchers (Patten et al., 2019), for the distributed 450 questions retrieved N=361 represented 80.2% of the respondent's rate, no recompense is given to avoid biases.

All the respondence replied via online using the following; WeChat, WhatsApp, QQ and email. This study constructs were six with thirty items adapted from  (Lin, Wang, Li, Shih, & Lin, 2016; Ho, Ocasio-Velázquez, & Booth, 2017). Some modifications were done to ensure questions suitable with crowdsourcing at the individual level. The constructs are; Attitude, Perceived Self-learner Usefulness, Peerceived Crowdsource, Broadband, Mobile Learning Behavior and Peformance Actualization. Each construct was measured with five or more using the 5point Likert score from strongly agree to strongly disagree (1-5), respectively(Beglar & Nemoto, 2014). The reasons for more questions skewed were to blur off ‘halo effects,’ accurate measurement effects, and minimized leniency effects (Sharma & Misra, 2017).  These are often followed by advanced statistical analysis techniques and to adhered to ensure ethics responsible accuracy of data.

Thes were coded into SPSSv.25 to sieve according to relaibility, correlation, and factor analyzed for CFA, the modifiied were extracted into Amos v. 24 for model fitness as well as estimations. The CFA went further to justify the structural equation model as presented in the paper. 

Usage Notes

Overview study trend and questionnaires:

Objectively, seeks to investigate students/learners respond inured in the interactive social learning nexus pro-COVID-19 communication crowdsourced technology in lockdown. Social learning in the media platforms among groups/clusters were explorative, behavioral developing learning communication as continuous knowledge sourcing mechanism. The questionnaires purposively survey international students in China universities during COVID-19. Achieving this, we use Perceived Crowdsource (PCS) integrated with Social Learning Theory Mediation of Mobile learning behaviors for actualizing performance. The researchers found major problem-solving tasks were resolved via mobile contents. The responses are expressed using the 5-point Likert scale, ranging from 1= "strong disagreed” to 5= “strong agreed” respectively.

Constructs Measurements and descriptions:

Attitude postulates a social learning human experience (behaviorism) to the scientific use of technology, the cognitive process juste milieu mobile learning. The forms and modelities learners always go through in technology. (Ajzen, Netemeyer, & Ryn, 1991; Schultze, U., 2002)

  1. I intend to use mobile learning anytime in the school environment.
  2. Using mobile learning eases retention and boredom during lockdown.
  3. Using mobile learning motivates my learning capacity.
  4. Mobile learning encourages my reading and discussion in chatrooms.
  5. I am always with my mobile device for information.
  6. I enjoy using mobile learning as I observed social distance protocol.

Perceived Self-learner Usefulness (PSLU) is the learner’s belief that using the mobile device will enhance personal interactivity with learing in academic performance (knowledge-centered)  (Venkatesh, Morris, Davis, & Davis, 2003; Bhattacherjee, A., Perols, J., & Sanford, 2008)

  1. Having access to my mobile inured continues knowledge-sourcing.
  2.  Mobile-learning is less difficult to search for information at fingertip.
  3. It’s easy using mobile to share questions and answers with colleagues.
  4. I effectively download learnable materials with a mobile device.
  5. Mobile-learning is companion and less stressful for reading during lockdown.

Perceived Crowdsourcing (PCS) is the belief that groups learning (crowd) association can source knowledge or exchange ideas in platforms discussions or  the internet.(Allen, B. J., Chandrasekaran, D., & Basuroy, 2018; Ho, Ocasio-Velázquez, & Booth, 2017)

  1. Crowd-based learning is network information sharing.
  2. Using mobile is easy to linkup crowd problem-solving.
  3. Mobile technology is individual based learning to solve a task.
  4. Community-based group discussions on mobile chat platforms is efficient.
  5. Teacher-learner discussed via mobile group chatting is helpful.
  6. Crowd internet content is easy way to access for knowledge.

Mobile Learning Behavior (MLB) is the use of mobile smartphone for reading and learning anytime and anywhere. (Ibrahim, 2018; Lin, Wang, Li, Shih, & Lin, 2016)

  1. I always use a mobile device to record information and knowledge sourcing.
  2. I use mobile interactive to chat for learning outside class.
  3. I use mobile information to share knowledge with colleagues.
  4. Mobile network compliments my crowd problem-solving.
  5. Using mobile chatroom for idea exchanges is helpful learning.
  6. Mobile learning motivates group tasks discussions.

Internet Broadband system (BBW) is the transmitter for effective userbility of mobile data. (Lin et al., 2016; Yan, 2019)

  1. Broadband fastens mobile internet surfing.
  2. Mobile broadband breaks edge easier than a computer.
  3. Mobile service quality is higher and user-friendly.
  4. Mobile data effectively ensure COVID-19 distance learning.
  5. The mobile system appeals to the user with effective broadband.

Behavior Intention Performance (BIP) Behavior has a relationship with the intention of a user’s performance to influence learning in use of technology. (Ajzen et al., 1991; Bhattacherjee, A., Perols, J., & Sanford, 2008)

  1. I intend to use mobile learning for crowdsourcing tasks.
  2. In the future, I will continue to use a mobile device for knowledge assessment.
  3. Moving around with access to mobile facilitates my learning at any time.
  4. I will use mobile learning in solving my academic problems.
  5. Mobile learning makes reading simple and easier anywhere.
  6. I intend to link up with teachers and colleagues via a mobile smart tool.