Call for Collaborative Research Proposals

 Overview and Purpose of Collaborative Projects

The Centre for Big Data on Technology-Mediated Education at Beijing Normal University (herein referred to as the Centre) is an initiative that is being funded by the Chinese government for a three-year term.

The purpose of the Centre is to support collaborative projects to inform theory, policy, teaching, learning and assessment, as well as to improve efficiencies, outcomes and understanding in the broad area of technology-mediated education.

Projects may combine or focus separately on theoretical and applied research to advance the field of big data in technology-mediated education. Theoretical research may focus on the development of policy or conceptual frameworks. Applied research may focus on devising analytic measures or methods and/or using such applications to provide evidence on teaching methods, learning behaviours or learning outcomes.

The Centre will provide financial support to collaborative projects led by domestic and international partners who will be profiled on the Centre website. Grants will be awarded for an amount of between USD $10,000 to $20,000 per project. Completed projects or on-going projects may be eligible for further funding. Grants will be led by the Centre and administered by Beijing Normal University.

The call is organised into 7 sections. In preparing a proposal please review each section:

 

  1. Areas of Interest

  2. Arrangements for Collaborative Projects

  3. Application: Short Proposal

  4. Application: Formal Proposal

  5. Review of Proposals

  6. Expectations of Partners

  7. Timeline and Contact

 

1.   Areas of Interest

Given the research potential on big data in technology-mediated education, there are a number of topics a project may address. The ensuing list presents a range of potential areas of inquiry. Considering the novelty of the field of big data in technology-mediated education, other related areas of inquiry are welcomed to be addressed in the project proposals, as outlined in the subsequent section.

Potential grant areas include, but are not limited to, the following six categories:

  • Large-scale analytical study of learners’ behaviour/performance/learning
    • Utilizing new models, tasks, methods, tools, measures and algorithms to advance the understanding of educational data mining or data wrangling;
    • Identifying patterns and make predictions that characterize students’ behaviours/performance online (e.g., learning management systems or MOOCs);
    • Exploring informal learning from heterogeneous sources (e.g. Twitter, Facebook, WeChat).
  • Educational assessment modelling based on big data and analytics
    • Advocating new approaches for educational assessment; increasing depth and accuracy of measurements of what students learn;
    • Pulling together complex data from heterogeneous sources to evaluate/measure students’ performance;
    • Generating assessment modelling to inform understanding of social activity in longitudinal logs;
    • Designing rich digital assessments for complex tasks.
  • Teaching Impact and intervention strategies
    • Integrating big data and analytics into teaching methods online;
    • Using big data and analytics to inform the design of courses for large learning populations;
    • Pioneering new intervention strategies to enhance online teaching and learning;
    • Using big data to tailor instruction to students’ needs, and providing individualized feedback.
  • Technical innovation of big data and analytics
    • Devising new models, tasks, methods, tools, measures and algorithms that advance the art of educational data mining or data wrangling;
    • Addressing technical challenges in visualization, natural language processing, social network analysis, systems optimization, etc.
  • Policy and strategy for systems-level deployment of big data in education
    • Investigating national or international policies and how they affect the use and implementation of big data for education;
    • Integrating big data and analytics into policy-making, policy implementation, privacy, security and ethics.
  • Theories and theoretical concepts of data-intensive educational research
    • Establishing an epistemology of big data in the discipline of education;
    • Conceptualizing the role of big data and analytics in the educational sector;
    • Creating new conceptual and theoretical frameworks to guide understanding about evidence-based teaching and learning in technology-mediated education.

2.   Arrangements for Funded Collaborative Projects

The following are three proposed arrangements for a funded collaborative project between the Centre and partner(s).

  1. Partner to co-lead or lead a comparative research project involving data derived from China and another country context (or other geographical area) that is relevant to technology-mediated education for large learning populations. The Centre will work collaboratively with partner(s) on the development of projects (including acquisition of data), data analysis, etc., based on mutual agreement.
  2. Partner to co-lead or lead an interdisciplinary project involving researchers working on systems science, statistics, mathematics, biology, and sociology, etc. to design a mechanism (e.g., theory, algorithm) for data mining, data wrangling, or data measures. Application to big data in technology-mediated education would be an integral component of this arrangement.  
  3. A project unrelated to 1. to 2. that is mutually agreed upon between the Centre and partner(s) that has relevance to advancing understanding of big data in technology-mediated education.

The timeline of a project will be for approximately one year. Start and end dates will be mutually agreed upon between the Centre and partner(s).

An important outcome of a project is the production of at least one journal article.

3.   Application: Short Proposal (due August 30, 2015)

The first stage of the application process is to submit a short proposal of no more than two pages in length (approx. 1,000 words). Please include a separate page(s) for references (references are excluded from the word count).

Short proposals should include:

  1. Experience working on big data and analytics (i.e., theoretical or applied)
  2. A short description of the project, its purpose, and relevance to technology-mediated education
  3. An explanation of research methods from rationale, techniques, instruments, analysis
  4. A short description of the suggested arrangements to work collaboratively with the Centre on big data and learning analytics

Short proposals are due August 30, 2015. All applicants will be notified on the outcome of submissions by September 15, 2015. For short-listed applicants, questions for clarity may need to be addressed in the formal proposal. Further details regarding submission for the formal proposal will be included with the notice of acceptance for short-listed applicants.

Proposals should be submitted via the easy chair system: https://easychair.org/conferences/?conf=call1

4. Application: Formal Proposal (due Oct. 15, 2015)

Short-listed applicants will be invited to submit a formal proposal. Proposals should be no more than six pages in length (approx. 3,000 words). Please include a separate page(s) for references (references are excluded from the word count).

Expectations for formal proposals are to expand on the short proposal by including the following criteria:

  1. Introduction and context of proposal
  2. Literature and existing research that informs proposal, gap in literature or field of inquiry
  3. Research questions
  4. Data that will be generated from project or implementation
  5. Methods to guide research
  6. Expected findings and contribution to scholarly work on big data and learning analytics
  7. Commitment to jointly produce one article in a journal (Social Science Citation Index)
  8. Proposed start date
  9. References

Formal proposals are due October 15, 2015. All applicants will be notified on the outcome of submissions by November 15, 2015. Further details regarding the award will be included in the notification.

Proposals should be submitted via the easy chair system: https://easychair.org/conferences/?conf=call1

5. Review of Proposals

Each proposal will be reviewed by a minimum of two reviewers who are members of the Advisory Board of the Centre.

Successful grants will address areas related to big data and technology-mediated education. Scoring will be based on the coverage of criteria listed in section 3 (Application: Short Proposal) or section 4 (Application: Formal Proposal).

6. Expectations of Partners

Partners are expected to jointly contribute to the development of the Centre and to scholarly work on big data and learning analytics.

This will include the production of one article in a journal (Social Science Citation Index) and a final report.

Partners will also be expected to present their research at a conference at Beijing Normal University in late 2016, or early 2017.

7. Timeline and Contact

July 15, 2015

Call announced

August 30, 2015

Short proposal deadline (approx. 1,000 words)

September 15, 2015

Notification of short-listed applications

October 15, 2015

Formal proposal deadline (approx. 3,000 words)

November 15, 2015

Final notification of successful grants

 

Queries may be addressed by email to the directors of the Centre:

Dr. Zhang Jingjing (Associate Professor, BNU): This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Zhang’s work focuses on developing data mining techniques (e.g., complex networking analysis) to explore human relationships and activities online, particularly in the learning sciences. This includes the impact on learning and collaboration in using open educational resources (OERs), massive open online courses (MOOCs), and knowledge visualization. Dr. Zhang holds a PhD from Oxford University and has also worked at the OECD in Paris and the UN in New York. For more information, please visit: http://fe.english.bnu.edu.cn/t003-ti-1-87-63.htm

Dr. Kirk Perris (Assistant Professor, BNU): This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Perris’s work focuses on quality assurance in open and distance learning, and the efficacy of using technology-mediated education to advance skill acquisition for semi-urban and rural populations. Dr. Perris holds a PhD from the University of Toronto and has worked  with the African Virtual University, the Commonwealth of Learning and the Indian Institute of Technology, Kanpur. For more information, please visit: http://fe.bnu.edu.cn/t002-ti-1-313-23.htm

 

 

 

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