Chengjiu Yin
Kobe University
Information Science and Technology Center

Calls for Papers (Special Issue): Educational Big Data and Learning Analytics on Journal of Interactive Learning Environments(ILE), indexed by SSCI . CFP in PDF

Guest Editors

Nian-Shing Chen, National Sun Yat-sen University, Taiwan
Chengjiu Yin, Kobe University, Japan
Pedro Isaias, The University of Queensland, Australia

The important dates are:

• Submission deadline: October 31, 2018
• 1st run review notification: December. 31, 2018
• Revised manuscript submission deadline: February 15, 2019
• 2nd run review notification: April. 15, 2019
• Revised manuscript submission deadline: May 31, 2019
• Acceptance notification: June 15, 2019
• Final camera-ready manuscript and required documents upload deadline: July 15, 2019
• Editorial Preface submitted by guest editor: July 31, 2019
• Publication date: September, 2019

Introduction

Recently, Educational Big Data (EBD) and Learning Analytics (LA) have become mainstream in many research fields. The concept of Big Data “refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.” (Manyika et al., 2011, p.1). Additionally, Big Data is often associated with key characteristics that go beyond the question of size, namely the 5 Vs: Volume, Velocity, Variety, Veracity and Value (Storey & Song, 2017). With the emergence of online learning environments, such as OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs), large volumes of data are being generated. Similarly, Learning Management Systems (LMSs) have caused a massive growth of the data that educational entities are required to manage. Since part of the students’ learning occurs externally data is dispersed among various platforms that operate with different standards, providers and degrees of access (Ferguson, 2012). Furthermore, the data is produced in a variety of formats, such as image, video, text and audio. This abundance and diversity of data can be gathered and stored to be processed through analytic methods (Daniel, 2015).

LA has emerged with EBD. LA “refers to the analysis and interpretation of data related to learners’ behaviors and interactions during the learning process, as well as learners’ profiles and the learning contexts they are situated in” (Hwang, Chu, & Yin, 2017, p.143). Before the emergence of LA, most research mainly concentrated on the analysis of learning outcomes, with considerable limitations in terms of data and sample sizes. LA focuses on extracting and analyzing meaningful information from EBD and as the data size is massive big data analytics tools must be used. LA helps to discover novel and potentially useful information in large amounts of unstructured data. With the LA results, teachers and students can change their teaching/learning strategies. With the new strategies, new EBD will be generated and new LA results can continuously be provided to teachers and students.

Previous research has argued that LA can facilitate the design of learning systems, educational materials and activities to improve educational effectiveness and optimize learning environments (Sutcliffe & Hart, 2016; Law & Larusdottir, 2015; Brajnik & Gabrielli, 2010; Greller & Drachsler, 2012). Romero and Ventura (2010) claim that each stakeholder obtains different benefits from LA:

• a. With regards to learners, they can benefit from personalization, recommendations of resources and activities, an improved learning experience and adaptive clues;
• b. Teachers can use LA to receive feedback, to examine both the learning and the behavior of the learners, to identify the students who need support, to determine which mistakes occur more often and to improve the effectiveness of some activities;
• c. Course developers can benefit from an evaluation of the courses’ structure and its impact for learning, to assess course materials, to identify the most valuable data mining methods according to different tasks and to develop learning models.
• d.With concern to administrators of educational institutions, they can use LA to organize resources, to improve their offer of educational programs and to assess both teachers and the effectiveness of curricula.

Considering these benefits of LA, many educational institutions have begun to collect EBD to perform LA and to incorporate it in their curricula. For example, the world’s first graduate program in LA was created at Teachers College of Columbia University (USA). A similar program was also created at the University of Pennsylvania (USA), named Learning Science & Technologies. Moreover, analogous programs also appeared in other universities, such as Northeastern University (USA), Boston University (USA), Carnegie Mellon University (USA), University of California Berkeley (USA), Georgetown University (USA), University of Sydney (Australian), University of Edinburgh (England). Some LA research groups were equally created: the University of Maryland (USA) created the LA Research Group; and Kyusyu University (Japan) created the LA Center.

With the growing popularity of LA and EBD as research subjects, the focus has been placed on prediction, structure discovery, and relationship mining. Research within these areas has been conducted at different levels, such as at course or degree level (Asif et al., 2017). In terms of prediction, a central research topic is predicting students’ educational outcomes. In structure discovery, the emphasis is on finding structure, patterns and data points in a set of data without any ground truth or a priori idea of what should be found (Baker & Inventado, 2014). Relationship mining involves discovering relationships between variables in a dataset. These relationships are seen as rules of data for later use (Bousbia & Belamri, 2013).

Despite the fact that there is a significant body of research in LA and EBD, there are still numerous challenges and questions that remain to be addressed: how can EBD be built and used by integrating different kinds of data from online learning systems such as learning management systems and game based learning systems; how to measure the adequateness and effectiveness of the LA results; how to promote the use of online learning systems for collecting EBD; how to protect personal privacy when collecting data from online learning systems, including privacy and security control policies; how to employ LA results to support learning designs; how to integrate learning theories and strategies with LA; how to employ LA approaches in various application domains.

Objective

The purpose of this special issue is to place an emphasis on the design, development and evaluation of LA using EBD. As LA reaches mainstream, it is crucial to examine design guidelines and best practices and development methods that can assist educators to benefit from the potential of the data that is available and the techniques that can endow it with meaning. Also, once the design and implementation stages are completed, it is key to explore their effectiveness with methods that can provide insight into the actual value of using LA and EBD. Hence, we aim to invite researchers who are engaged in exploring design methodologies, development methods and effectiveness evaluation of LA and/or EBD applications which can provide solutions to the abovementioned issues and challenges.

Recommended Topics

We cordially invite authors to submit high quality manuscripts related to LA and/or EBD research. Topics of interests include, but are not limited to, the following:
•Automatic assessment of student knowledge
•Comparing the behavioral patterns of the students with different personal factors, such as learning achievements, cognitive styles, learning styles or motives
•Data integration / cleansing methods and management tools for collecting meaningful Educational Big Data
•Data mining in social and collaborative learning
•Data mining with emerging pedagogical environments such as educational games, MOOCs
•Deriving representations of domain knowledge from data
•Detecting and addressing students’ affective and emotional states
•Developing learning models or assessment models based on learning analytics results
•Evaluations and assessment of Learning Analytics Results
•Evaluations of the efficacy of curriculum and interventions
•Generic frameworks, techniques, research methods, and approaches for Learning Analytics
•Identifying students’ behavioral patterns
•Identifying learning strategies from Educational Big Data
•Integrating data mining and educational theory
•Investigating the issue of personal privacy protection
•Learning Design based on Learning Analytics Results
•Multi-modal learning environments and sensor analysis
•Practices for the adaptation of Learning Analytics results to enhance teaching/learning environments
•Predictions and process mining from Educational Big Data
•Privacy and security management for open Educational Big Data
•Proposing new learning analytics algorithms for learning environments or learning technologies
•Providing support for teachers and other stakeholders
•Theories and models in Learning Analytics
•The loop between education data research and educational outcomes
•Visualizations of learning activities with Educational Big Data

Submission Procedure

Please submit your papers to the ILE journal at Interactive Learning Environments(ILE)
Please select the title of the special issue: "Special Issue- Educational Big Data and Learning Analytics ".

All submissions and inquiries should be directed to the attention of:
Nian-Shing Chen, National Sun Yat-sen University, Taiwan
nianshing@gmail.com
Chengjiu Yin, Kobe University, Japan
yinchengjiu@gmail.com

About extended version of conference paper

If your conference paper is invited to submit your extended version to our SI, please make sure that your extended version includes the following bullet points:

1. the extended version of the conference papers should have at least 45% difference from the conference version and the difference should be reflected in the analysis, design, system, experiment/evaluation, data collection and analysis, and findings and discussions;
2. the paper should be NOT including too theoretical contents (e.g., math, symbols and equations) and the paper needs to have running examples with simulated or real datasets to explain correspondent equations, definitions, atoms, theorems, and proofs if those theoretical content is extremely important for readers to understand their research;
3. the paper should be strongly connected to the practical usages in distance education or e-learning, which means, a research is used for people to learn in the classroom (or with others at same place together at the same time) would never be accepted;
4. the paper needs to have experiment or evaluation with proper quantitative and/or qualitative data analysis, findings and discussions;