Content-based methods in peer assessment of open-response questions to grade students as authors and as graders

Luaces, Oscar; Díez, Jorge; Alonso‐Betanzos, Amparo; Troncoso, Alicia; Bahamonde, Antonio

doi:10.1016/j.knosys.2016.06.024

Cited by 21 publications

(23 citation statements)

References 20 publications

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“…For a large number of grades, the evaluation score can be obtained using the average or median, but for a small set, that occurs in the majority of workgroups, the average cannot be effective in the calculation of the individual scores. Also, most students lack in evaluation skills, that can jeopardize the individual and collective marks of the group work (Luaces et al, 2017). Also, there are costs associated to planning and resources that distort the equal distribution of the students' performance (Vossen, 2018).…”

Section: Parameters Descriptionmentioning

confidence: 99%

Self and Peer E-Assessment

Babo

Rocha

Fitas

et al. 2021

International Journal of Information and Communication Technology Education

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In recent years, universities and other academic institutions have been performing collaborative learning to improve the students' skills, competencies, and learning outcomes. The problem-based learning approach is a learning method where students can work in groups to develop these skills. However, when working in groups, the students are not always being assessed accordingly to their contributions to the work development. Therefore, self and peer assessment are becoming a common practice in academic institutions. With the technology evolution and the emerging of assessment software tools, the evaluators' work is becoming easier. This paper presents seven different tools, as well as their features and functionalities. To evaluate and compare these tools, some parameters are presented and described, based on usability and user experience definitions. It concludes that there is a demand to develop a freeware tool, with parameters not presented in the existing evaluation tools to assist the lecturer in the assessment.

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Section: Parameters Descriptionmentioning

confidence: 99%

Self and Peer E-Assessment

Babo

Rocha

Fitas

et al. 2021

International Journal of Information and Communication Technology Education

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show abstract

“…This function estimates a consensus sequence of tasks that calculates a rating which can be easily converted into a rating function for each task. describe a factorization approach to scoring, as a scalable method that capable on handling very high volumes of data [22]. This method is also capable of representing the content of the open response using the vector space model of the answer.…”

Section: Matrix Factorizationmentioning

confidence: 99%

Algorithm of Assessment System in MOOCs Learning: Literature Review

Sumarsono¹,

Sutrisno²,

Fatwanto³

2021

Proceedings of the International Conference on Science and Engineering (ICSE-UIN-SUKA 2021)

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Assessment of learners in the MOOCs online learning platform creates problems for teachers in using appropriate assessment strategies. The change from the traditional face-to-face learning model to the MOOCs online learning model does not necessarily apply traditional classroom assessment strategies to online classes. The traditional and online classroom assessment strategies are far different, teachers and participants do not meet face-to-face and do not know each other physically. The algorithm has a very important role in automating the assessment in accordance with the assessment strategy that is in accordance with the characteristics of online learning MOOCs. The purpose of this study is to identify the use of this type of algorithm in supporting the assessment strategy in the MOOCs system. The method used in this study is through a literature study using the Scopus and sciendirect database sources published between January 2013 to May 2021. The results show that formative and summative assessments are carried out automatically using algorithms classified as artificial intelligence. This can be used as a reference for the development of learning and assessment of adaptive MOOCs in accordance with the characteristics and learning objectives of the course.

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“…To increase student performance and engagement, some researchers (Alario-Hoyos et al, 2014;Luacesa et al, 2017;M. Zhang et al, 2019) presented work focused on the design of recommender systems only.…”

Section: Student Performance Recommender Systemsmentioning

confidence: 99%

Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

Khalid

Lundqvist

Yates

2020

IRRODL

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In recent years, massive open online courses (MOOCs) have gained popularity with learners and providers, and thus MOOC providers have started to further enhance the use of MOOCs through recommender systems. This paper is a systematic literature review on the use of recommender systems for MOOCs, examining works published between January 1, 2012 and July 12, 2019 and, to the best of our knowledge, it is the first of its kind. We used Google Scholar, five academic databases (IEEE, ACM, Springer, ScienceDirect, and ERIC) and a reference chaining technique for this research. Through quantitative analysis, we identified the types and trends of research carried out in this field. The research falls into three major categories: (a) the need for recommender systems, (b) proposed recommender systems, and (c) implemented recommender systems. From the literature, we found that research has been conducted in seven areas of MOOCs: courses, threads, peers, learning elements, MOOC provider/teacher recommender, student performance recommender, and others. To date, the research has mostly focused on the implementation of recommender systems, particularly course recommender systems. Areas for future research and implementation include design of practical and scalable online recommender systems, design of a recommender system for MOOC provider and teacher, and usefulness of recommender systems.

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Content-based methods in peer assessment of open-response questions to grade students as authors and as graders

Cited by 21 publications

References 20 publications

Self and Peer E-Assessment

Self and Peer E-Assessment

Algorithm of Assessment System in MOOCs Learning: Literature Review

Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

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