1. What have the authors contributed in "Scoring assessment in elearning" ?
Farhan, Sohail Jabbar, Muhammad Aslam, Shehzad Khalid, Mohammad Hammoudeh, 3 Murad Khan, Kijun Han this paper.
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IoT-based students interaction framework using attention-scoring assessment in eLearning
Muhammad Farhan,Muhammad Farhan,Sohail Jabbar,Sohail Jabbar,Sohail Jabbar,Muhammad Aslam,Mohammad Hammoudeh,Mudassar Ahmad,Shehzad Khalid,Murad Khan,Kijun Han +10 more
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TL;DR: IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning) and the learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms.
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About: This article is published in Future Generation Computer Systems. The article was published on 01 Feb 2018. and is currently open access. The article focuses on the topics: Educational technology & Synchronous learning.
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Figures
Table 1. Variable means for student data 278 
Table 2. Cluster centers for the attention assessment variables 290 
Table 8. Two-sample correlation test results using “total score” and frontal “face or profile face” 385 variables 386 
Table 9. Correlation of the data using Pearson method 395 FPS Frontal or profile Number of eyes Face Eyes EOC Total score 
Fig 3. Analysis of response of attentiveness using all variables of ASM using ANOVA test. This chart shows 335 participating variables for classifying the attentiveness of the student. 336 337 
Fig 4. Correlation for attentiveness measure for input variables for the collected data 406 407 Each range is investigated in more detail alongside cases from both industry practice and scholarly 408 research. Numerous learning and innovation specialists are excited about the possibility of information 409 driving the student experience as shown in Fig 5. Student data analysis empowers a learning framework 410 that only gives the appropriate measure of direction. Various specialists warn against using an 411 examination alone to identify which topics or abilities students work on next or whether they progress to 412 the next stage. 413
Citations
Analysis of student behavior in learning management systems through a Big Data framework
Magdalena Cantabella,Raquel Martínez-España,Belén López Ayuso,Juan Antonio Yáñez,Andrés Muñoz +4 more
TL;DR: A case study is presented at the Catholic University of Murcia, where student behavior in the past four academic years was analyzed according to learning modality, considering the number of accesses to the LMS, tools employed by students and their associated events.
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Internet of Things for Sustainable Smart Education: An Overview
TL;DR: The present research explains the ways in which IoT has been applied for the benefit of school managers, teachers, and learners, showcased in the recent literature.
Investigating the Impact of the Internet of Things in Higher Education Environment
TL;DR: In this article, the authors proposed an assessment model based on six hypotheses, including their definitions and descriptions, and validated against the Romanian higher education system, as well as a set of survey data.
Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology
Farhan Ullah,Farhan Ullah,Junfeng Wang,Muhammad Farhan,Sohail Jabbar,Zhiming Wu,Shehzad Khalid +6 more
TL;DR: A new plagiarism detection technique between C++ and Java source codes based on semantics in multimedia-based e-Learning and smart assessment methodology is proposed and the experimental results show better semantic similarity results for plagiarism Detection based on comparison.
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A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings
Marcelo Guerra Hahn,Silvia Margarita Baldiris Navarro,Luis de la Fuente Valentín,Daniel Burgos +3 more
TL;DR: In this article, the authors analyzed 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool and gave an overview of the trends, challenges, and open questions in this research area.
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