scholarly journals Shape of Educational Data: Interdisciplinary Perspectives

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Colleen M. Ganley ◽  
Sara A. Hart
Keyword(s):  
Student Learning ◽  
Educational Psychology ◽  
Learning Analytics ◽  
Guest Editorial ◽  
Online Courses ◽  
Special Section ◽  
Online Systems ◽  
The Third ◽  
Online Platforms ◽  
Insight Into

This paper is a guest editorial for a special section that forms the proceedings of the Shape of Educational Data meeting. This special section features papers that apply methods from multiple fields — including mathematics, computer science, educational psychology, and learning analytics — to describe and predict student learning in online platforms. The special section is organized such that the first set of articles discusses different online learning systems (WEPS, WeBWorK, and inVideo) and data that can be analyzed from these systems. The second set of articles involves descriptions of topological data analyses that can be helpful to researchers in learning analytics and educational psychology to better model student learning in online courses. The third set of articles uses data obtained from online systems to study factors related to student learning. Due to these multiple approaches, we can gain insight into the types of data available, the ways in which we can measure particular constructs related to learning using these data, and the ways we can analyze these data, including statistical approaches and visualizations.

scholarly journals Let’s Grow Together: Tutorials on Learning Analytics Methods

2016 ◽  
Vol 3 (3) ◽  
pp. 5-8 ◽  
Author(s):  
Dragan Gasevic ◽  
Mykola Pechenizkiy
Keyword(s):  
Network Analysis ◽  
Learning Analytics ◽  
Guest Editorial ◽  
Pattern Mining ◽  
Special Section ◽  
Visual Learning ◽  
Coherence Analysis ◽  
Text Coherence ◽  
Social Media Text ◽  
Visual Learning Analytics

This paper is a guest editorial into a special section that offers a collection of tutorials on methods that can be used in learning analytics. The special section is prepared as a response to the growing need of learning analytics practitioners and researchers to learn and use novel methods. In spite of this need, papers that systematically introduce some of the methods have been underrepresented in the literature. Specifically, the special section features papers that introduce epistemic network analysis, automated content and network analysis of social media, text coherence analysis with Coh-Metrix, microgenetic analysis with sequence pattern mining, and design of visual learning analytics guided by educational theory informed goals.

Guest Editorial: Special Section on Learning Analytics

2017 ◽  
Vol 10 (1) ◽  
pp. 3-5 ◽  
Author(s):  
Dragan Gasevic ◽  
George Siemens ◽  
Carolyn Penstein Rose
Keyword(s):  
Learning Analytics ◽  
Guest Editorial ◽  
Special Section

scholarly journals Correlation between Grades Earned and Time in Online Courses

2017 ◽  
Vol 21 (4) ◽  
Author(s):  
Lin Carver ◽  
Keya Muhkerjee ◽  
Robert Lucio
Keyword(s):  
Online Education ◽  
Graduate Education ◽  
Student Performance ◽  
Learning Analytics ◽  
Online Courses ◽  
Course Delivery ◽  
Education Students ◽  
Insight Into ◽  
Specific Letter ◽  
Synchronous Online

Online education is rapidly becoming a significant method of course delivery in higher education. Consequently instructors are analyzing student performance in an attempt to better scaffold student learning. Learning analytics can provide insight into online students’ course behaviors. Archival data from 167 graduate level education students enrolled in 4 different programs and 9 different online courses was analyzed in an attempt to determine if there was a correlation between their grades and the time spent in specific areas within the course: the total time within the course, the course modules, document repository, and synchronous online sessions. Data was analyzed by total time in course, time in modules, time in document repository, and time in the online synchronous discussions as well as by program. Time spent in each component did not correlate with the specific letter grade, but did correlate with earning an A or not earning an A. The sample was composed of students from four different graduate education programs: Educational Leadership, Reading, Instructional Design, and Special Education. Variations were found between programs, but the differences did not significantly correlate with the grade earned in the course. A logical progression revealed that of all the predictor variables, only time spent in synchronous online sessions showed as a significant predictor of receiving an A in the course. This is important information for instructor when providing scaffolding for students.

scholarly journals Method of Text Content Development in Creation of Professionally Oriented Online Courses for Oil and Gas Specialists

2019 ◽  
Vol 14 (17) ◽  
pp. 143
Author(s):  
Yuliya Murzo ◽  
Svetlana Sveshnikova ◽  
Natalia Chuvileva
Keyword(s):  
Distance Learning ◽  
Critical Review ◽  
Oil And Gas ◽  
Online Courses ◽  
Online Course ◽  
Online Systems ◽  
Online Platforms ◽  
Content Development ◽  
Three Stages ◽  
Text Content

The article analyzes the experience of text content development for an online course created for oil and gas professionals. The article features a critical review of the current situation in online systems of distance learning, establishes principles of creating professionally oriented language curricula, as well as requirements to the functionality of online platforms that enable creation of such courses and their delivery to the audience. The work provides a detailed analysis of text content development principles and criteria. It presents three stages of developing text content for the first module of a professionally oriented online course.

No fear of George Kingsley Zipf

10.1558/37291 ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 242-263
Author(s):  
Stefano Rastelli ◽  
Kook-Hee Gil
Keyword(s):  
Minimalist Program ◽  
Classroom Research ◽  
Language Design ◽  
Efficient Computation ◽  
Generative Linguistics ◽  
The Third ◽  
Factor Effect ◽  
Language Classroom ◽  
Recent Developments ◽  
Insight Into

This paper offers a new insight into GenSLA classroom research in light of recent developments in the Minimalist Program (MP). Recent research in GenSLA has shown how generative linguistics and acquisition studies can inform the language classroom, mostly focusing on what linguistic aspects of target properties should be integrated as a part of the classroom input. Based on insights from Chomsky’s ‘three factors for language design’ – which bring together the Faculty of Language, input and general principles of economy and efficient computation (the third factor effect) for language development – we put forward a theoretical rationale for how classroom research can offer a unique environment to test the learnability in L2 through the statistical enhancement of the input to which learners are exposed.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

Marketing Responsible Drinking Effectively to Young Adults

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Colby Doyle ◽  
Matthew Gaudet ◽  
Dominic Lay ◽  
Amber McLeod ◽  
Robert Schaeffer
Keyword(s):  
Quantitative Research ◽  
Drinking Patterns ◽  
Major Question ◽  
The Public ◽  
The Third ◽  
Current Consumption ◽  
Sample Data ◽  
Vague Quantifiers ◽  
Insight Into ◽  
Alberta Health

The primary goal of this research is to identify and examine the components of responsible drinking advertisements. We will examine industry and government related advertisements as we try to understand one of our major questions: does the source influence the validity of the message? The next group of major questions that we will be looking to answer is how are the vague quantifiers used in responsible drinking campaigns interpreted by the public?  How many drinks do people consider “too much?” What does “drink responsibly” really mean? The third major question is whether or not an individual’s current consumption patterns of alcohol have any effect on how individuals assess responsible drinking campaigns. Our qualitative research has indicated that social influences can be strongly related with drinking patterns; this will be further examined in our quantitative research. Also, we will be looking into some of the psychology behind industry and government sponsored advertisements as well as gathering and interpreting information from a sample of our target demographic. Our target demographic consists of both male and females between the ages 18-24. Our literature review and qualitative analysis gave us good insight into some of the potential answers to our questions. We will use these potential answers from our previous research to guide us as we attempt to conduct conclusive research based on a sample data of 169 individuals. Our findings will aid us in developing conclusions and recommendations for Alberta Health Services.

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