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

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals What if Classroom Instruction Becomes a Thing of the Past?

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Enrique Mu
Keyword(s):  
Latin American ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
University Education ◽  
Massive Open Online ◽  
Face To Face ◽  
The Past ◽  
Virtual Instruction ◽  
The U.S ◽  
Latin American And Caribbean

Until recently, there was no doubt about what constituted a university education and how it was carried out. Suddenly, the COVID-19 pandemic occurred, and in a few weeks, not only education, but the entire world changed. In the new normal, post-pandemic world, it is possible that teaching face-to-face courses will be the exception, not the rule, in the U.S. and the Latin American and Caribbean regions. Furthermore, this virtual instruction will possibly be at massive levels with tens or hundreds of thousands of students at a time, modeled after massive open online courses (MOOCs).

scholarly journals What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

2019 ◽  
Vol 33 ◽  
pp. 6674-6681 ◽  
Author(s):  
Irene Li ◽  
Alexander R. Fabbri ◽  
Robert R. Tung ◽  
Dragomir R. Radev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Courses ◽  
Specific Topic ◽  
Reading List ◽  
University Courses

Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of “what should one learn first,”we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available 1. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.

scholarly journals Initial trends in enrolment and completion of massive open online courses

2014 ◽  
Vol 15 (1) ◽  
Author(s):  
Katy Jordan
Keyword(s):  
Online Courses ◽  
Rapid Development ◽  
Good Deal ◽  
Massive Open Online Courses ◽  
Completion Rates ◽  
Massive Open Online ◽  
Factors Affecting ◽  
The Public ◽  
The Past ◽  
Course Length

<p>The past two years have seen rapid development of massive open online courses (MOOCs) with the rise of a number of MOOC platforms. The scale of enrolment and participation in the earliest mainstream MOOC courses has garnered a good deal of media attention. However, data about how the enrolment and completion figures have changed since the early courses is not consistently released. This paper seeks to draw together the data that has found its way into the public domain in order to explore factors affecting enrolment and completion. The average MOOC course is found to enroll around 43,000 students, 6.5% of whom complete the course. Enrolment numbers are decreasing over time and are positively correlated with course length. Completion rates are consistent across time, university rank, and total enrolment, but negatively correlated with course length. This study provides a more detailed view of trends in enrolment and completion than was available previously, and a more accurate view of how the MOOC field is developing.</p>

Opinion of Computer Science Instructors and Students on MOOCs in an Indian University

2018 ◽  
Vol 47 (2) ◽  
pp. 205-212 ◽  
Author(s):  
Pushp Sra ◽  
Pinaki Chakraborty
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Undergraduate Students ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Computer Networking ◽  
Classroom Teaching ◽  
Massive Open Online ◽  
Effective Form ◽  
Science Instructors

Massive open online courses (MOOCs) have lately emerged as an effective form of distance education. Computer science is one of those disciplines in which MOOCs are quite common. We conducted a survey to know the opinion of computer science instructors and undergraduate students on MOOCs in an Indian university in March 2018. Of the 26 instructors and 273 students who participated, 73% instructors and 87% students reported to have attended MOOCs. We found that 50% instructors and 72% students considered MOOCs to be a more comprehensive source of knowledge and 54% instructors and 76% students felt that MOOCs let students learn faster when compared to courses taught in a classroom. Moreover, 58% instructors believed that the courses they teach in classrooms can also be taught effectively through MOOCs. The instructors and students appreciated several aspects of MOOCs. However, the students had an opinion that MOOCs can augment classroom teaching but cannot replace it. MOOCs on computer programming (22%), artificial intelligence (9%), and computer networking (8%) were found to be particularly popular among the students.

Knowledge Discovery of Student Sentiments in MOOCs and Their Impact on Course Performance

2014 ◽  
Author(s):  
Conrad S. Tucker ◽  
Bryan Dickens ◽  
Anna Divinsky
Keyword(s):  
Student Performance ◽  
Language Processing ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Massive Open Online ◽  
Course Performance ◽  
Student Interactions ◽  
Computing Device ◽  
Digital World ◽  
Textual Data

The objective of this research is to mine textual data (e.g., online discussion forums) generated by students enrolled in Massive Open Online Courses (MOOCs) in order to quantify students’ sentiment, in relation to their course performance. Massive Open Online Courses (MOOCs) are free to anyone with a computing device and a means of connecting to the internet and serve as a new paradigm for distance based education. While student interactions in traditional based brick and mortar classes are readily observable by students and instructors, quantifying the sentiments expressed by students in MOOCs remains challenging. This is in part due to the quantity of textual data being generated by students enrolled in MOOCs, in addition to a lack of quantitative methodologies that discover latent, previously unknown knowledge pertaining to student interactions and sentiments in the digital world. The authors of this work introduce a data mining driven methodology that employs natural language processing techniques and text mining algorithms to quantify students’ sentiments, based on their textual data provided during course assignment discussions. The researchers of this work aim to help educators understand the factors that may impact student performance, team interactions and overall learning outcomes in digital environments such as MOOCs.

scholarly journals Adoption of MOOCs by Emerging Countries Seeking Solutions to University Overcrowding: Literature Review and Feedback from the First Scientific MOOC Held by Sidi Mohammed Ben Abdullah University – Fez, Morocco

2020 ◽  
Vol 15 (22) ◽  
pp. 78
Author(s):  
Khadija Naji ◽  
Abdelali Ibriz ◽  
Youssef Mourdi
Keyword(s):  
Online Courses ◽  
Large Mass ◽  
Emerging Countries ◽  
Massive Open Online Courses ◽  
Educational Model ◽  
Massive Open Online ◽  
Face To Face ◽  
The Past ◽  
Development Costs ◽  
Academic Services

The adoption of various forms of distance education, particularly MOOCs (an acronym for Massive Open Online Courses), by universities worldwide has continuously gained momentum over the past decade. This is due not only to the importance of maintaining a parallel educational model alongside face-to-face courses in order to complete students’ training, but also in response to the limits of academic infrastructure faced with an increasingly large mass of learners, typically in emerging countries. Universities view MOOCs as a remedy to this dilemma—one which promises reasonable development costs—especially taking into account the ubiquity of the internet and digital communication tools. In a country such as Morocco, whose university capacity has been stretched to 186%, the quest to dematerialize lectures can support universities in producing well-rounded professional profiles as well as improving institutional and academic services overall. In this paper, we present the feedback from Sidi Mohammed Ben Abdellah University concerning its first scientific MOOC, launched within the framework of the MarocUniversitéNumérique (Morocco Digital University) or MUN project in collaboration with the France UniversitéNumérique (France Digital University) or FUN platform. The objectives of this paper are threefold: to assess the possibility of adopting further MOOCs in a Moroccan setting, to seek insight on the profiles of learners who have completed MOOCs and to draw lessons in order to improve future experiences.

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.

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