Facilitating Social Collaboration in Mobile Cloud-Based Learning: A Teamwork as a Service (TaaS) Approach

Sun, Geng; Shen, Jun

doi:10.1109/tlt.2014.2340402

Cited by 54 publications

(37 citation statements)

References 49 publications

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“…Without a doubt, mobile learning is getting extensive acceptance by learners and gradually becoming a major learning means [11] [12]. The benefits of m-learning are well studied in numerous researches.…”

Section: Mobile Learning In Moocmentioning

confidence: 99%

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MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Sun

Cui

Yong

et al. 2018

IEEE Trans. Serv. Comput.

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Section: Mobile Learning In Moocmentioning

confidence: 99%

“…The details of TaaS has been presented in [12], including a genetic algorithm enclosed to discover computational choices of grouping strategy [17]. In this paper we will focus on the other SaaS, namely Micro Learning as a Service (MLaaS) which will be introduced in the rest of the paper.…”

Section: Comprehensive Vle Frameworkmentioning

confidence: 99%

MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Sun

Cui

Yong

et al. 2018

IEEE Trans. Serv. Comput.

Self Cite

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“…Hence, generating effective scripts for CSCL is time consuming, challenging and require expertise knowledge [7,8,13]; these concerns become exponential when a large class is considered. Therefore researchers' interest had drawn for CSCL scripting [1,3] and computer supported orchestration mechanisms [14] by identifying and modelling both practitioner and participant constraints. Intrinsic constraints are guarded by the pedagogical rationale behind the script and extrinsic constraints can be induced from diverse sources such as educational context, technological factors or even from arbitrary decisions [2].…”

Section: Multiple Constraints Frameworkmentioning

confidence: 99%

“…Figure 2 illustrates a conceptual model that elaborates potential sources to retrieve soft constraints within a given learning scenario. Parameters of learner profile such as personal details (marks, level of education, country of origin) or individual learning style or previous experiences or interests (skills) are used as group formation policies [8,14]. With the advent of open APIs exposed by different social media such as Facebook, Twitter, LinkedIn gives an opportunity for educational tool developers to extract public profile details of the learner [15].…”

Section: Generic Model For Soft Constraintsmentioning

confidence: 99%

A Multiple Constraints Framework for Collaborative Learning Flow Orchestration

Manathunga

Hernández‐Leo

2016

Lecture Notes in Computer Science

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Abstract. Collaborative Learning Flow Patterns (e.g., Jigsaw) offer sound pedagogical strategies to foster fruitful social interactions among learners. The pedagogy behind the patterns involves a set of intrinsic constraints that need to be considered when orchestrating the learning flow. These constraints relate to the organization of the flow (e.g., Jigsaw pattern -a global problem is divided into sub-problems and a constraint is that there need to be at least one expert group working on each sub-problem) and group formation policies (e.g., groups solving the global problem need to have at least one member coming from a different previous expert group). Besides, characteristics of specific learning situations such as learners' profile and technological tools used provide additional parameters that can be considered as context-related extrinsic constraints relevant to the orchestration (e.g., heterogeneous groups depending on experience or interests). This paper proposes a constraint framework that considers different constraints for orchestration services enabling adaptive computation of orchestration aspects. Substantiation of the framework with a case study demonstrated the feasibility, usefulness and the expressiveness of the framework.

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“…The use of multimedia and online technologies provides the opportunity for sharing information [17], and can help bridge the gap with deficiencies in assumed knowledge PAPER UNDERSTANDING HOW STUDENTS USE AND APPRECIATE ONLINE RESOURCES IN THE TEACHING LABORATORY [18]. It can also help cater for the difficult task of providing learning opportunities that reflect students' individual needs [19].…”

Section: Introductionmentioning

confidence: 99%

Understanding How Students Use and Appreciate Online Resources in the Teaching Laboratory

Nikolic

2015

Int. J. Onl. Eng.

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Abstract-The internet is a great resource student's use for learning. Reasons include the ease in searching with sites such as Google, or the vast collection of informative videos on YouTube. The teaching laboratory can also benefit from online resources, especially when students are deficient in prerequisite knowledge. The benefits are greatest when there are non-standard learning paths, and multiple entry points into a degree. This study undertakes a mixed methods research approach to try and understand how students use and appreciate an online resource, called the Training Laboratory, designed to support learning in the engineering teaching laboratory. The targeted resources are used to help support students as well as the laboratory teaching assistants (called laboratory demonstrators). The study finds that such resources are used by a substantial number of students to aid learning, increasing productivity, and improving teaching. The availability of such targeted resources leads to an improved student experience.

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Facilitating Social Collaboration in Mobile Cloud-Based Learning: A Teamwork as a Service (TaaS) Approach

Cited by 54 publications

References 49 publications

MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

A Multiple Constraints Framework for Collaborative Learning Flow Orchestration

Understanding How Students Use and Appreciate Online Resources in the Teaching Laboratory

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