Machine learning models to predict the dropouts in Massive Open Online Courses

2018 ◽  
Author(s):  
Avinash Kashyap ◽  
Ashalatha Nayak
Keyword(s):  
Machine Learning ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Learning Models ◽  
Massive Open Online ◽  
Machine Learning Models

scholarly journals Identification of Affective States in MOOCs

2018 ◽  
Vol 6 (12) ◽  
pp. 39-55
Author(s):  
Napoliana Souza ◽  
Gabriela Perry
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Analytics ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Signal Recognition ◽  
Affective States ◽  
Massive Open Online ◽  
Data Intensive ◽  
Physiological Signal

Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction,  no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent.

scholarly journals Educational Advances in Artificial Intelligence

AI Magazine ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 127 ◽  
Author(s):  
Laura E. Brown ◽  
David Kauchak
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Massive Open Online ◽  
The Past ◽  
Software Packages ◽  
Pedagogical Innovations ◽  
K 12

The emergence of massive open online courses has initiated a broad national-wide discussion on higher education practices, models, and pedagogy.  Artificial intelligence and machine learning courses were at the forefront of this trend and are also being used to serve personalized, managed content in the back-end systems. Massive open online courses are just one example of the sorts of pedagogical innovations being developed to better teach AI. This column will discuss and share innovative educational approaches that teach or leverage AI and its many subfields, including robotics, machine learning, natural language processing, computer vision, and others at all levels of education (K-12, undergraduate, and graduate levels).  In particular, this column will serve the community as a venue to learn about the Symposium on Educational Advances in Artificial Intelligence (EAAI) (colocated with AAAI for the past four years); introductions to innovative pedagogy and best practices for AI and across the computer science curricula; resources for teaching AI, including model AI assignments, software packages, online videos and lectures that can be used in your classroom; topic tutorials introducing a subject to students and researchers with links to articles, presentations, and online materials; and discussion of the use of AI methods in education shaping personalized tutorials, learning analytics, and data mining

scholarly journals Improving Students Performance in Small-Scale Online Courses - A Machine Learning-Based Intervention

2020 ◽  
Vol 2 (2) ◽  
pp. 80
Author(s):  
Sepinoud Azimi ◽  
Carmen-Gabriela Popa ◽  
Tatjana Cucić
Keyword(s):  
Machine Learning ◽  
Online Courses ◽  
Dropout Rate ◽  
Intervention Strategies ◽  
Massive Open Online Courses ◽  
Small Scale ◽  
Young Generation ◽  
Massive Open Online ◽  
Pedagogical Tool ◽  
Overall Performance

<p class="0abstract"><span lang="EN-US">The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in-class teaching is becoming less popular with the young generation – the generation that wants to choose when, where and at what pace they are learning. As such, many universities are moving towards taking their courses, at least partially, online. However, online courses, although very appealing to the younger generation of learners, come at a cost. For example, the dropout rate of such courses are higher than that of more traditional ones, and the reduced in-person interaction with the teachers results in less timely guidance and intervention from the educators. Machine learning (ML)-based approaches have shown phenomenal successes in other domains. The existing stigma that applying ML-based techniques requires a large amount of data seems to be a bottleneck when dealing with small-scale courses with limited amounts of produced data. In this study, we show not only that the data collected from an online learning management system could be well utilized in order to predict students’ overall performance but also that it could be used to propose timely intervention strategies to boost the students’ performance level. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students’ progress for the better. We also present an assistive pedagogical tool based on the outcome of this study, to assist in identifying challenging students and in suggesting early intervention strategies.</span></p>

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Education and the Relation to the Outside: A Little Real Reality

2015 ◽  
Vol 9 (3) ◽  
pp. 273-300 ◽  
Author(s):  
David Savat ◽  
Greg Thompson
Keyword(s):  
Performance Metrics ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Educational Institutions ◽  
Special Issue ◽  
Massive Open Online ◽  
Transformative Teaching ◽  
Control Society ◽  
Brave New World ◽  
Radical Transformation

One of the more dominant themes around the use of Deleuze and Guattari's work, including in this special issue, is a focus on the radical transformation that educational institutions are undergoing, and which applies to administrator, student and educator alike. This is a transformation that finds its expression through teaching analytics, transformative teaching, massive open online courses (MOOCs) and updateable performance metrics alike. These techniques and practices, as an expression of control society, constitute the new sorts of machines that frame and inhabit our educational institutions. As Deleuze and Guattari's work posits, on some level these are precisely the machines that many people in their day-to-day work as educators, students and administrators assemble and maintain, that is, desire. The meta-model of schizoanalysis is ideally placed to analyse this profound shift that is occurring in society, felt closely in the so-called knowledge sector where a brave new world of continuous education and motivation is instituting itself.

cMOOCs and Global Learning: An Authentic Alternative

2013 ◽  
Vol 17 (2) ◽  
Author(s):  
Carol Yeager ◽  
Betty Hurley-Dasgupta ◽  
Catherine A. Bliss
Keyword(s):  
Online Courses ◽  
Lessons Learned ◽  
Massive Open Online Courses ◽  
Research Review ◽  
Press Coverage ◽  
Open Educational Resource ◽  
Educational Resource ◽  
Global Learning ◽  
Massive Open Online ◽  
Definition Of

Massive Open Online Courses (MOOCs) continue to attract press coverage as they change almost daily in their format, number of registrations and potential for credentialing. An enticing aspect of the MOOC is its global reach. In this paper, we will focus on a type of MOOC called a cMOOC, because it is based on the theory of connectivism and fits the definition of an Open Educational Resource (OER) identified for this special edition of JALN. We begin with a definition of the cMOOC and a discussion of the connectivism on which it is based. Definitions and a research review are followed with a description of two MOOCs offered by two of the authors. Research on one of these MOOCs completed by a third author is presented as well. Student comments that demonstrate the intercultural connections are shared. We end with reflections, lessons learned and recommendations.

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