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1 |
Data-driven detection and characterization of communities of accounts collaborating in MOOCs
شناسایی و توصیف مبتنی بر داده جوامع حسابهایی که در MOOC همکاری میکنند-2021 Collaboration is considered as one of the main drivers of learning and it has been broadly studied
across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs
has risen exponentially during the last years and there have been a number of works focused
on studying collaboration. However, these previous studies have been restricted to the analysis
of collaboration based on the forum and social interactions, without taking into account other
possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work
we performed a case study with the goal of implementing a data-driven approach to detect and
characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based
on their submission times to quizzes as an indicator of collaboration, and applied it to data from
two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in
couples and larger communities exhibiting different types of associations between user accounts. The
characterization of these user accounts suggested that some of them might represent genuine online
learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or
multiple account cheating might be present. These findings call for additional research on the study
of the kind of collaborations that can emerge in online settings.
keywords: تجزیه و تحلیل یادگیری | داده کاوی آموزشی | یادگیری مشارکتی | دوره های آنلاین گسترده باز | هوش مصنوعی | Learning analytics | Educational data mining | Collaborative learning | Massive open online courses | Artificial intelligence |
مقاله انگلیسی |
2 |
The impact of learner-, instructor-, and course-level factors on online learning
تأثیر عوامل یادگیرنده ، مربی و دوره در یادگیری آنلاین-2020 The number of K-12 students taking online courses has been increasing tremendously over the
past few years. However, most research on online learning either compares its overall effectiveness
to that of traditional learning, or examines perceptions or interactions using self-reported
data; and very few studies have looked into the relationships between the elements of K-12 online
courses and their students’ learning outcomes. Based on student-, instructor-, and course-level
data from 919 students enrolled in eight online high-school English language and literature
courses, the results of hierarchical linear modeling and content analysis found that project-based
assignments and high-level knowledge activities were beneficial to learning outcomes – though
not necessarily among students who took these courses for credit-recovery purposes. The paper
also discusses implications for both online course-design practices and future research on predictors
of online-learning success. Keywords: K-12 online education | Online course design | English language and literature | Higher-level knowledge activities |
مقاله انگلیسی |
3 |
AI in Cybersecurity Education: A Systematic Literature Review of Studies on Cybersecurity MOOCs
هوش مصنوعی در آموزش امنیت سایبری: مروری بر ادبیات سیستماتیک مطالعات مربوط به امنیت سایبری MOOC-2020 Machine learning (ML) techniques are changing
both the offensive and defensive aspects of cybersecurity. The
implications are especially strong for privacy, as ML approaches
provide unprecedented opportunities to make use of collected
data. Thus, education on cybersecurity and AI is needed. To investigate
how AI and cybersecurity should be taught together, we
look at previous studies on cybersecurity MOOCs by conducting
a systematic literature review. The initial search resulted in 72
items and after screening for only peer-reviewed publications
on cybersecurity online courses, 15 studies remained. Three of
the studies concerned multiple cybersecurity MOOCs whereas
12 focused on individual courses. The number of published work
evaluating specific cybersecurity MOOCs was found to be small
compared to all available cybersecurity MOOCs. Analysis of
the studies revealed that cybersecurity education is, in almost
all cases, organised based on the topic instead of used tools,
making it difficult for learners to find focused information on
AI applications in cybersecurity. Furthermore, there is a gab
in academic literature on how AI applications in cybersecurity
should be taught in online courses. Index Terms: cybersecurity | MOOC | machine learning | AI | systematic literature review |
مقاله انگلیسی |
4 |
Review of ontology-based recommender systems in e-learning
مرور سیستمهای پیشنهادی مبتنی بر هستی شناسی در یادگیری الکترونیکی-2019 In recent years there has been an enormous increase in learning resources available online
through massive open online courses and learning management systems. In this context, personalized
resource recommendation has become an even more significant challenge, thereby increasing
research in that direction. Recommender systems use ontology, artificial intelligence,
among other techniques to provide personalized recommendations. Ontology is a way to model
learners and learning resources, among others, which helps to retrieve details. This, in turn,
generates more relevant materials to learners. Ontologies have benefits of reusability, reasoning
ability, and supports inference mechanisms, which helps to provide enhanced recommendations.
The comprehensive survey in this paper gives an overview of the research in progress using
ontology to achieve personalization in recommender systems in the e-learning domain. Keywords: Human-computer interface | Intelligent tutoring systems | Computer-mediated communication | Cooperative/collaborative learning |
مقاله انگلیسی |
5 |
Uncovering Student Perceptions of a First-Year Online Writing Course
کشف مشاهدات دانش آموز در یک دوره آنلاین نوشتن سال اولی-2018 This article examined student perceptions of the Writing Program Administrators (WPA) learning outcomes for first-year writing through a fully online first-year writing course. A second research question explored how the course content focused on technology, visual rhetoric, and social media impacted students’ overall perceptions about their learning. The method used in this study is qualitative in nature, based on a Likert-scale survey and end of semester open-ended surveys with students. The findings indicate that students perceived their abilities to improve not only in the four areas delineated by the WPA outcomes, but also through the ability to see writing as the primary method of communication and have more time to reflect in an online environment. Findings also suggest that instructor feedback and relevant course content both positively impact student perceptions of an online course. This article concludes with encouragement for additional research and studies relating to first-year writing courses in an online environment.
keywords: First-year writing| Online course| WPA outcomes |
مقاله انگلیسی |
6 |
Assessing learners satisfaction in collaborative online courses through a big data approach
ارزیابی رضایتمندی دانشجویان در دوره های آنلاین همکاری از طریق رویکرد داده ای بزرگ-2018 Monitoring learners satisfaction (LS) is a vital action for collecting precious information and design
valuable online collaborative learning (CL) experiences. Todays CL platforms allow students for per
forming many online activities, thus generating a huge mass of data that can be processed to provide
insights about the level of satisfaction on contents, services, community interactions, and effort. Big Data
is a suitable paradigm for real-time processing of large data sets concerning the LS, in the final aim to
provide valuable information that may improve the CL experience. Besides, the adoption of Big Data
offers the opportunity to implement a non-intrusive and in-process evaluation strategy of online courses
that complements the traditional and time-consuming ways to collect feedback (e.g. questionnaires or
surveys). Although the application of Big Data in the CL domain is a recent explored research area with
limited applications, it may have an important role in the future of online education. By adopting the
design science research methodology, this article describes a novel method and approach to analyse
individual students contributions in online learning activities and assess the level of their satisfaction
towards the course. A software artefact is also presented, which leverages Learning Analytics in a Big
Data context, with the goal to provide in real-time valuable insights that people and systems can use to
intervene properly in the program. The contribution of this paper can be of value for both researchers
and practitioners: the former can be interested in the approach and method used for LS assessment; the
latter can find of interest the system implemented and how it has been tested in a real online course.
Keywords: Big data ، Clustering ، Collaborative learning ، Learning analytics ، Learning satisfaction ، Sentiment analysis |
مقاله انگلیسی |
7 |
Mining theory-based patterns from Big data: Identifying self regulated learning strategies in Massive Open Online Courses
الگوهای مبتنی بر نظریه کاوش از داده های بزرگ: شناسایی خود استراتژی های یادگیری تنظیم شده در دوره های گسترده آنلاین باز-2018 Big data in education offers unprecedented opportunities to support learners and advance research in the
learning sciences. Analysis of observed behaviour using computational methods can uncover patterns
that reflect theoretically established processes, such as those involved in self-regulated learning (SRL).
This research addresses the question of how to integrate this bottom-up approach of mining behavioural
patterns with the traditional top-down approach of using validated self-reporting instruments. Using
process mining, we extracted interaction sequences from fine-grained behavioural traces for 3458
learners across three Massive Open Online Courses. We identified six distinct interaction sequence
patterns. We matched each interaction sequence pattern with one or more theory-based SRL strategies
and identified three clusters of learners. First, Comprehensive Learners, who follow the sequential
structure of the course materials, which sets them up for gaining a deeper understanding of the content.
Second, Targeting Learners, who strategically engage with specific course content that will help them
pass the assessments. Third, Sampling Learners, who exhibit more erratic and less goal-oriented
behaviour, report lower SRL, and underperform relative to both Comprehensive and Targeting
Learners. Challenges that arise in the process of extracting theory-based patterns from observed
behaviour are discussed, including analytic issues and limitations of available trace data from learning
platforms.
Keywords: Self-regulated learning ، Learning strategies ، Process mining ، Massive open online courses |
مقاله انگلیسی |
8 |
Mining theory-based patterns from Big data: Identifying self regulated learning strategies in Massive Open Online Courses
الگوهای مبتنی بر تئوری کاوش از داده های بزرگ: شناسایی راهبردهای یادگیری خود تنظیم شده در دوره های گسترده آنلاین باز-2018 Big data in education offers unprecedented opportunities to support learners and advance research in the
learning sciences. Analysis of observed behaviour using computational methods can uncover patterns
that reflect theoretically established processes, such as those involved in self-regulated learning (SRL).
This research addresses the question of how to integrate this bottom-up approach of mining behavioural
patterns with the traditional top-down approach of using validated self-reporting instruments. Using
process mining, we extracted interaction sequences from fine-grained behavioural traces for 3458
learners across three Massive Open Online Courses. We identified six distinct interaction sequence
patterns. We matched each interaction sequence pattern with one or more theory-based SRL strategies
and identified three clusters of learners. First, Comprehensive Learners, who follow the sequential
structure of the course materials, which sets them up for gaining a deeper understanding of the content.
Second, Targeting Learners, who strategically engage with specific course content that will help them
pass the assessments. Third, Sampling Learners, who exhibit more erratic and less goal-oriented
behaviour, report lower SRL, and underperform relative to both Comprehensive and Targeting
Learners. Challenges that arise in the process of extracting theory-based patterns from observed
behaviour are discussed, including analytic issues and limitations of available trace data from learning
platforms.
Keywords: Self-regulated learning ، Learning strategies ، Process mining ، Massive open online courses |
مقاله انگلیسی |
9 |
Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections
پل تجزیه و تحلیل یادگیری و پردازش شناختی برای طبقه بندی داده های بزرگ در مجموعه های ویدئویی میکرو یادگیری-2018 Moving towards the next generation of personalized learning environments requires intelligent ap
proaches powered by analytics for advanced learning contexts with enriched digital content. Micro
Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm
for delivering short educational videos in small pre-organized chunks over time, so that learners can get
knowledge in a manageable way. However, with the ever-increasing number of videos, it has become
challenging to arrange and search them according to specific categories. In this paper, we get around the
problem by bridging Learning Analytics and Cognitive Computing to analyze the content of large video
collections, going over traditional term-based methods. We propose an efficient and effective approach
to automatically classify a collection of educational videos on pre-existing categories which uses (i) a
Speech-to-Text tool to get video transcripts, (ii) Natural Language Processing and Cognitive Computing
methods to extract semantic concepts and keywords from video transcripts for their representation, and
(iii) Apache Spark as Big Data technology for scalability. Several classifiers are trained on the feature
vectors extracted by Cognitive Computing tools. Then, we compared our approach with other combi
nations of state-of-the-art feature types and classifiers over a large-scale dataset we collected from
Coursera. Considering the experimental results, we expect our approach can facilitate the development
of Learning Analytics tools powered by Cognitive Computing to support content managers on micro
learning video management while improving how learners search videos.
Keywords: Cognitive Computing ، Big Data technologies ، Micro-learning video ، Multi-class classification ، Learning Analytics ، Video classification |
مقاله انگلیسی |
10 |
تاثیر یک بازی شبیه سازی ERP روی یادگیری آنلاین
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 22 افزایش روزافزون آموزش آنلاین این موضوع را تاکید می کند که استفاده از ابزار رو – در – روی سنتی برای دانشجویان به داخل کلاس های مجازی گسترده شده است. دانشجویان کسب و کار در دوره های آنلاین MBA (مدیریت اجرایی تجاری) در دو دانشگاه با تصمیم گیری با استفاده از بازی ساخت شبیه سازی ERP (برنامه ریزی منابع شرکتی) SAP مورد بررسی قرار گرفتند. از دانشجویان قبل و بعد از تجربه شبیه سازی خود در 5 بُعد شامل نحوه نگرش به SAP و چندین بُعد در دانش برنامه ریزی منابع شرکتی رأی گیری شد. پی برده شد که دانشجویان نگرش خود به SAP و دانش برنامه ریزی مدیریت اجرایی را با کامل کردن بازی شبیه سازی سه – دوره ای، افزایش داده اند. این نتایج برای مربیان کسب و کار در دوره های آنلاین، قابل توجه هستند چون نشان می دهد که افزایش یادگیری به علت شبیه سازی ERP نه تنها در کلاس های آموزشی رو – در – رو اتفاق می افتد بلکه در یک محیط غیرهمزمان نیز اتفاق می افتد. مقایسه نتایج با نتایج گزارش شده در مطالعات قبلی که با کلاس های سنتی سروکار داشتند بینش ها و موضوعات بالقوه بیشتری را برای تحقیقات آتی آشکار می کند.
کلیدواژه ها: یادگیری آنلاین | تدریس آنلاین غیرهمزمان | ERP | شبیه سازی ERP | SAP |
مقاله ترجمه شده |