Implementation and Performance Evaluation of an Intelligent Online Argumentation Assessment System

Huang, Chenn‐Jung; Wang, Yu-Wu; Huang, Tz-Hau; Liao, Jia-Jian; Chen, Chunhua; Weng, Chuan-Hsiang; Chu, Yu-Jen; Chien, Chiao-Yun; Shen, Hung-Yen

doi:10.1109/icece.2010.632

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Recently, a lot of studies on eye movements [15] [16] [17] and machine learning techniques [18] [19] have been done to detect different kinds of learner's behaviors during learning. However, detection of learners' concentration with a feedback to an instructor has not been given much attention in distance learning environments.…”

Section: Related Workmentioning

confidence: 99%

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Daniel¹,

Kamioka²

2017

JCC

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The trend of distance learning education has increased year by year because of the rapid advancement of information and communication technologies. Distance learning system can be regarded as one of ubiquitous computing applications since the learners can study anywhere even in mobile environments. However, the instructor cannot know if the learners comprehend the lecture or not since each learner is physically isolated. Therefore, a framework which detects the learners' concentration condition is required. If a distance learning system obtains the information that many learners are not concentrated on the class due to the incomprehensible lecture style, the instructor can perceive it through the system and change the presentation strategy. This is a contextaware technology which is widely used for ubiquitous computing services. In this paper, an efficient distance learning system, which accurately detects learners' concentration condition during a class, is proposed. The proposed system uses multiple biological information which are learners' eye movement metrics, i.e. fixation counts, fixation rate, fixation duration and average saccade length obtained by an eye tracking system. The learners' concentration condition is classified by using machine learning techniques. The proposed system has performed the detection accuracy of 90.7% when Multilayer Perceptron is used as a classifier. In addition, the effectiveness of the proposed eye metrics has been confirmed. Furthermore, it has been clarified that the fixation duration is the most important eye metric among the four metrics based on the investigation of evaluation experiment.

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Section: Related Workmentioning

confidence: 99%

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Daniel¹,

Kamioka²

2017

JCC

View full text Add to dashboard Cite

show abstract

“…For an academic review of essays, systems such as Criterion , Writing Pal , or SWORD are available to provide general support across several linguistic dimensions. For argument analysis, there are tools like MARGOT to identify argument components or the system in which uses keywords to analyze arguments in natural language texts. Argument systems such as GAIL are used for learning to argue in human genetics, and also provide feedback on structure and content.…”

Section: Introductionmentioning

confidence: 99%

Automatic argument assessment of final project reports of computer engineering students

García-Gorrostieta

González-López

2018

Comp Applic In Engineering

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Arguments in final course project reports written by engineering students are an essential feature of academic texts. Further revisions of instructors reveal to the students their mistakes, such as a lack of argumentation. However, the depth of revisions is often limited by the time availability of the instructor. In this paper, we present a system for argument assessment embedded in an Internet‐based Moodle course with Learning Tools Interoperability (LTI) standard, to help students improve argumentation in the problem statement, justification and conclusion sections of their final project report. The system identifies argumentative paragraphs along with argumentation level and provides recommendations to improve their writing. The analysis is achieved employing machine learning techniques with lexical features trained using an annotated collection of student writings. We performed a pilot test to compare control and experimental groups. Both groups consisted of undergraduate students of computer engineering programs from two different universities. We observed that using the argument assessment system increased the number of arguments in the experimental group. We discuss some further improvements for the system.

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Towards Automatic Assessment of Argumentation in Theses Justifications

García-Gorrostieta

González-López

2017

Data Driven Approaches in Digital Education

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Implementation and Performance Evaluation of an Intelligent Online Argumentation Assessment System

Cited by 6 publications

References 21 publications

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Automatic argument assessment of final project reports of computer engineering students

Towards Automatic Assessment of Argumentation in Theses Justifications

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