The Technology, Knowledge and Learning (TKNL) journal invites submissions for a special issue “Big data in higher education: Research methods and analytics supporting the learning journey” to be published in 2017.
One of the promises of big data in higher education is to enable a new level of evidence-based research into learning and instruction and make it possible to gain highly detailed insight into student performance and their learning trajectories as required for personalizing and adapting curriculum as well as assessment. In the new era of data-driven learning and teaching, researchers need to be equipped with an advanced set of competencies that encompass areas needed for computationally intensive research (e.g., data-management techniques for big data, working with interdisciplinary teams who understand programming languages as well as cognitive, behavioral, social and emotional perspectives on learning) and professional knowledge (including heuristics) that incline a researcher toward computational modeling when tackling complex research problems.
This special issue on data analytics focuses on the enabling computational approaches and challenges in research that support the journey of a learner from pre-university experiences, to marketing and recruitment, to personalized learning, adaptive curriculum and assessment resources, to effective teaching, to post-university life-long learning.
Authors are encouraged to submit any of the manuscript types outlined below, including Work-in-Progress reports which highlight implemented systems in higher education and Emerging Technology reports focusing on data analytics applications. Interested scholars should submit a 1-page proposal including a tentative title, information about contributing author(s), abstract, article type (see below), keywords, and key references to David Gibson (email@example.com) by 15 July 2016 – early submissions are encouraged. All proposals will be reviewed by the special issue review board who will recommend full submissions from among the proposals. All full manuscript submissions will undergo rigorous double-blind peer review by at least three reviewers of the special issue review board and regular TKNL reviewers who will recommend revisions or acceptance.
Integrative Review: An integrative review provides an overview and synthesizes relevant literature using an adequate method such as: Chronological (organized around a specific timeline), publication type (grouped by sources of research evidence), trends (identify different streams of the research over time), thematic (organized around topics or ideas), or methodological (grouped by research studies or projects). Integrative review manuscripts are expected to be between 4,000 and 8,000 words including references, tables, and figures.
Emerging Technology Report: An emerging technology reports reviews new developments in educational technology by assessing the potentials and key challenges for leading digital learning environments. Emerging technology report manuscripts are limited to 3,000 words including references, tables, and figures.