scholarly journals The 2010 KDD Cup Competition Dataset: Engaging the machine learning community in predictive learning analytics

2016 ◽  
Vol 3 (2) ◽  
pp. 312-316 ◽  
Author(s):  
John Stamper ◽  
Zachary A Pardos
Keyword(s):  
Data Mining ◽  
Educational Technology ◽  
Learning Community ◽  
Learning Analytics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Computing Machinery ◽  
Predictive Learning ◽  
Cognitive Tutors

In the spring of 2010, the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data-mining (KDD) selected a dataset from an educational technology for its annual competition. The competition, titled “Educational Data Mining Challenge”, tasked participants with predicting the correctness of student answers to questions within an Intelligent Tutoring System (ITS) from The Cognitive Tutors suite of tutors. This challenge was hosted by the PSLC DataShop, and included data provided by the Carnegie Learning Inc., producers of The Cognitive Tutors. Consisting of over 9GB of student data this was the largest KDD Cup dataset up to that point in time. The competition brought in 655 competitors submitting 3,400 solutions. Five years later, we believe the competition dataset has been the most often cited from an educational technology platform.

scholarly journals Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview

2016 ◽  
Vol 11 (03) ◽  
pp. 67 ◽  
Author(s):  
Igor Jugo ◽  
Božidar Kovačić ◽  
Vanja Slavuj
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Knowledge Domain ◽  
Systems Architecture ◽  
Tutoring Systems ◽  
E Learning ◽  
Mining Tools

Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.

Educational Data Mining and Learning Analytics for Improving Online Learning Environments

2020 ◽  
Vol 64 (1) ◽  
pp. 500-504
Author(s):  
Yancy Vance Paredes ◽  
Robert F. Siegle ◽  
I-Han Hsiao ◽  
Scotty D. Craig
Keyword(s):  
Data Mining ◽  
Educational Technology ◽  
Human Factors ◽  
Learning Environments ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Educational Systems ◽  
User Centered Design ◽  
High Quality ◽  
Online Learning Environments

The proliferation of educational technology systems has led to the advent of a large number of datasets related to learner interaction. New fields have emerged which aim to use this data to identify interventions that could help the learners become efficient and effective in their learning. However, these systems have to follow user-centered design principles to ensure that the system is usable and the data is of high quality. Human factors literature is limited on the topics regarding Educational Data Mining (EDM) and Learning Analytics (LA). To develop improved educational systems, it is important for human factors engineers to be exposed to these data-oriented fields. This paper aims to provide a brief introduction to the fields of EDM and LA, discuss data visualization and dashboards that are used to convey results to learners, and finally to identify where human factors can aid other fields.

Modeling the Interplay Between Knowledge and Affective Engagement in Students

2014 ◽  
Vol 3 (2) ◽  
pp. 56-74
Author(s):  
Sarah E. Schultz ◽  
Ivon Arroyo
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective State ◽  
Educational Data Mining ◽  
Affective Engagement ◽  
Tutoring System ◽  
Learning Session ◽  
Psychology Literature ◽  
And Behavior

Two major goals in Educational Data Mining are determining students' state of knowledge and determining their affective state as students progress through the learning session. While many models and solutions have been explored for each of these problems, relatively little work has been done on examining these states in parallel, even though the psychology literature suggests that it is an interplay of both of these states that influences how a student performs and behaves. This work proposes a model that takes into account the performance and behavior of students when working with an Intelligent Tutoring System in order to track both knowledge and engagement and tests it on data from two different systems and explores the usefulness of such models.

scholarly journals Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

2016 ◽  
Vol 3 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Paulo Blikstein ◽  
Marcelo Worsley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Online Courses ◽  
Educational Data Mining ◽  
Multimodal Learning ◽  
Learning Tasks ◽  
Education Data ◽  
Multimodal Learning Analytics ◽  
High Degree ◽  
Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

Learning Analytics

2018 ◽  
pp. 5158-5168
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

Educational Data Mining Techniques and Applications

2021 ◽  
pp. 234-251
Author(s):  
M. Govindarajan
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
At Risk Students ◽  
Educational Data Mining ◽  
Learning Needs ◽  
Institutional Performance ◽  
Clear Cut ◽  
Curriculum Renewal ◽  
Improving Teaching And Learning

Educational data mining (EDM) creates high impact in the field of academic domain. EDM is concerned with developing new methods to discover knowledge from educational and academic database and can be used for decision making in educational and academic systems. EDM is useful in many different areas including identifying at risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This chapter discusses educational data mining, its applications, and techniques that have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning. The techniques and applications discussed in this chapter will provide a clear-cut idea to the educational data mining researchers to carry out their work in this field.

scholarly journals Educational Data Mining and Learning Analytics

2014 ◽  
pp. 61-75 ◽  
Author(s):  
Ryan Shaun Baker ◽  
Paul Salvador Inventado
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining
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