مقاله انگلیسی رایگان در مورد آنالیز رفتار دانشجویی در سیستم های مدیریت یادگیری – الزویر ۲۰۱۹

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله آنالیز رفتار دانشجویی در سیستم های مدیریت یادگیری از طریق یک چارچوب کلان داده
عنوان انگلیسی مقاله Analysis of student behavior in learning management systems through a Big Data framework
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۳۸ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۶۳۹ در سال ۲۰۱۷
شاخص H_index ۸۵ در سال ۲۰۱۹
شاخص SJR ۰٫۸۴۴ در سال ۲۰۱۹
رشته های مرتبط مهندسی کامپیوتر، فناوری اطلاعات
گرایش های مرتبط الگوریتم و محاسبات، مدیریت سیستم های اطلاعات
نوع ارائه مقاله
ژورنال
مجله / کنفرانس نسل آینده سیستم های کامپیوتری – Future Generation Computer Systems
دانشگاه Department of Computer Science – Universidad Cat´olica de Murcia – Spain
کلمات کلیدی سیستم های مدیریت یادگیری، MapReduce، الگوریتم Apriori، تحلیل یادگیری الکترونیکی، رفتار دانشجویی، داده های بزرگ
کلمات کلیدی انگلیسی Learning management systems, MapReduce, Apriori algorithm, E-learning analytics, Student behavior, Big Data
شناسه دیجیتال – doi
https://doi.org/10.1016/j.future.2018.08.003
کد محصول E10259
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فهرست مطالب مقاله:
Highlights
Abstract
Keywords
۱ Introduction
۲ Related work
۳ Framework based on big data for analyzing Sakai data
۴ Case study
۵ Conclusion and future work
Acknowledgments
Appendix.
References
Vitae

بخشی از متن مقاله:
Abstract

In recent years, learning management systems (LMSs) have played a fundamental role in higher education teaching models. A new line of research has been opened relating to the analysis of student behavior within an LMS, in the search for patterns that improve the learning process. Current e-learning platforms allow for recording student activity, thereby enabling the exploration of events generated in the use of LMS tools. This paper presents a case study conducted at the Catholic University of Murcia, where student behavior in the past four academic years was analyzed according to learning modality (that is, oncampus, online, and blended), considering the number of accesses to the LMS, tools employed by students and their associated events. Given the difficulty of managing the large volume of data generated by users in the LMS (up to 70 GB in this study), statistical and association rule techniques were performed using a Big Data framework, thus speeding up the statistical analysis of the data. The obtained results are demonstrated using visual analytic techniques, and evaluated in order to detect trends and deficiencies in the use of the LMS by students.

Introduction

A current tendency in higher education consists of the analysis and processing of data relating to the activity generated by users through the use of learning management systems (LMSs). The significant amount of data extracted from 5 these platforms provide fundamental information that can aid both teachers and students in improving their educational goals. One of the main problems at present is the analysis of this information, owing to two main factors: the already mentioned large volume of data available, and the different formats of these data, particularly for the management of unstructured data. 10 According to several studies (see, for example, [1, 2]), there exists a need for analytical tools to help to interpret LMS data and provide new knowledge for improving and even designing new e-learning techniques and methodologies. Before manipulating such information, it is also important to explore and select the necessary data from the LMS, according to the goals to be achieved. 15 The main objective of this work is to design and implement a framework based on big data technologies to identify the behavior patterns of LMS users and illustrate them in an intuitive and intelligible manner. For this purpose, we define the following steps: • Data preprocessing, by studying the data to be extracted from the LMS 20 and its storage in a big data platform. • Data analysis and identification of pattern recognition techniques that may provide value in the educational context. • Presentation of the obtained results according to suitable visual analytics techniques and tools. 25 For developing these steps, we have considered data processing guided by e-learning analytics, in which the connections among educational techniques, learning concepts and educational data mining are studied [3, 4]. Within this field, the areas most relevant to our work are learning analytics and visual analytics. The former aids us in data processing for discovering connections among 30 students, teachers and the learning process, with the purpose of creating recommendations that improve the overall educational process. The latter uses visual interfaces to illustrate the results obtained from analytical reasoning, facilitating an understanding of the new knowledge and aiding the users in discovering new relations or possible irregularities [5]. Here, we take a further step forward 35 in the use of e-learning analytics by integrating big data techniques into the educational data analysis.

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