Multimedia based student-teacher smart interaction framework using multi-agents in eLearning

Iqbal, Muhammad Munwar; Saleem, Yasir; Naseer, Kashif; Kim, Mucheol

doi:10.1007/s11042-017-4615-z

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…ACM Q2 [23], [24], [25], [26] IEEE Q1 [27], [28], [29], [30], [31], [32], [33] Science Direct Q1 [34], [35], [36], [13], [33], [37], [38] Springer Q1 [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [18], [55], [56], [57], [58] Others Q1 [59], [60] Q2 [61], [62], [63], [64],…”

Section: Publisher Quartile Papermentioning

confidence: 99%

“…Asynchronous [18], [25], [26], [28], [29], [30], [31], [33], [34], [35], [39], [40], [41], [43], [44], [45], [46], [47], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [63], [66], Synchronous [24], [27], [38], [62] Other [48] Role Peer [18], [47], [55], [58] Tutor [24], [28], [29], [30][26], [31], [34], [35], [37], [38],…”

Section: Environ -Mentmentioning

confidence: 99%

“…Learning Material [26], [30], [31], [34], [38], [39], [40], [41], [45], [46], [50], [51], [52], [53], [54], [56], [57], [58], [59], [60], [63], Guidance [25], [31], [18], [37], [38], [46][50], [54], [58], [61] Motivation/ Encouragement [24], [50], [51], [52], [54], [55], [56], [61], [62], [63] Hint [18], [47], [55], [62] Question/ assessment [18], [44], [47], [48], [49], [55], [59], Other…”

Section: Interventionmentioning

confidence: 99%

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Emotion-Related Pedagogical Agent: A Systematic Literature Review

Septiana,

Mutijarsa,

Putro

et al. 2024

IEEE Access

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The purpose of this systematic literature review (SLR) is to examine more deeply the various learning interventions provided by pedagogical agents and their relation to emotional aspects. A pedagogical agent is a software agent that provides guidance, feedback, or intervention to learners in digital environments. Pedagogical agents have the potential to address issues in computer-based learning, particularly online learning, which often neglects affective aspects, such as the emotions of its users. Most research on pedagogical agents has focused on their visual design, types of feedback, and other empirical aspects. However, this research has not examined what underlies these agents' ability to provide interventions personalized to learner's emotions. This paper explores the extent to which pedagogical agents have addressed learners' emotional needs. It also identifies opportunities and challenges for further research on interventions by pedagogical agents personalized to learners' emotional states. The study's research questions include: 1) To what extent does research exist on models, frameworks, or architectures for pedagogical agents, especially those related to emotions? 2) How are pedagogical agents represented, what types of interventions do they use, and how do these interventions affect learners' emotions? 3) What kinds of inputs are used to activate the pedagogical agent's functions? This SLR applied the Kitchenham method to select reference sources from 2013 to 2023 and was indexed by Scopus in the Q1 to Q4 range. Our review revealed the absence of a specific model for mapping out interventions tailored to the learner's emotional needs. Most existing pedagogical agents provide learning interventions that are less adaptive and personalized based on the learner's emotional state and are applied to asynchronous learning systems such as e-learning. There are still very few pedagogical agents that use real-time input technology by utilizing artificial intelligence to recognize the emotional state of learners so that they can trigger an adaptive and personalized intervention.

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Section: Publisher Quartile Papermentioning

confidence: 99%

Section: Environ -Mentmentioning

confidence: 99%

Section: Interventionmentioning

confidence: 99%

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Emotion-Related Pedagogical Agent: A Systematic Literature Review

Septiana,

Mutijarsa,

Putro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…The enhancement and conformities required to run the computation-intensive jobs need a testbed composed of very costly hardware. The investigation is also helpful for the exascale calculation in future [5]. The technique depends on the presumption of "common" bond lengths and points, deviation from which prompts strain, and the presence of torsional connections and appealing or potentially terrible van der Waals and dipolar powers between nonfortified molecules [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Cloud computing data security and privacy are enhanced by using the Single Sign-On approach. This approach uses identity management techniques like OAuth, Figure 1: Multimedia big data particular levels [4,5]. OpenID, and SAML for the enhancement of cloud users' security and privacy [1].…”

Section: Introductionmentioning

confidence: 99%

Augmenting High‐Performance Mobile Cloud Computations for Big Data in AMBER

Iqbal

Ali

Alfawair

et al. 2018

Wireless Communications and Mobile Computing

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Big data is an inspirational area of research that involves best practices used in the industry and academia. Challenging and complex systems are the core requirements for the data collation and analysis of big data. Data analysis approaches and algorithms development are the necessary and essential components of the big data analytics. Big data and high-performance computing emergent nature help to solve complex and challenging problems. High-Performance Mobile Cloud Computing (HPMCC) technology contributes to the execution of the intensive computational application at any location independently on laptops using virtual machines. HPMCC technique enables executing computationally extreme scientific tasks on a cloud comprising laptops. Assisted Model Building with Energy Refinement (AMBER) with the force fields calculations for molecular dynamics is a computationally hungry task that requires high and computational hardware resources for execution. The core objective of the study is to deliver and provide researchers with a mobile cloud of laptops capable of doing the heavy processing. An innovative execution of AMBER with force field empirical formula using Message Passing Interface (MPI) infrastructure on HPMCC is proposed. It is homogeneous mobile cloud platform comprising a laptop and virtual machines as processors nodes along with dynamic parallelism. Some processes can be executed to distribute and run the task among the various computational nodes. This task-based and databased parallelism is achieved in proposed solution by using a Message Passing Interface. Trace-based results and graphs will present the significance of the proposed method.

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