The Bloomsbury Colleges | PhD Studentships | Studentships 2016 | Using learning analytics to expose and better support the practice-based teaching and learning process in STEM education (UCL IOE) & (BBK)
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Using Learning Analytics to Expose and Better Support the Practice-Based Teaching and Learning Process in STEM Education

Principle Supervisor: Professor Rosemary Luckin (UCL IOE)

Co-Supervisor: Professor Alex Poulovassilis (Bbk)

The design of Project based learning activities that support learners’ participation in scientific practice is central to the pedagogy of teaching STEM subjects. Activities emphasise the engagement of learners in projects that are personally meaningful, support engagement in authentic scientific inquiry activities, are collaborative, open-ended, and rely largely on learners’ self-regulation (e.g. Mulholland et al., 2012; Scardamalia, 2001). The PBL approach therefore requires that participating students have good collaborative skills and sufficient metacognitive awareness to steer them through the problem space in a manner that enables their learning. As a result the potential outcomes for the students are not merely cognitive in terms of their increased understanding of the subject matter of the problem, but also there are advances in the transferable twenty first century skills of communication, collaboration and critical thinking. However, the hands-on, learner-driven and open-ended nature of Project based learning creates challenges for tracking the learning process and for both formative assessment and the provision of appropriate support. Technology can help address these challenges while also supporting learners to complete the project. For example, SAMs (https://samlabs.me) are Bluetooth enabled electronics modules that can be used for project-based learning activities. Easy to use software enables learners to link together different modules and produce behaviours e.g. to create a bird feeder, weather station or remote controlled car. Each action by learners is captured as a data point by the SAMs technology, this data is available for collation and analysis to chart the progress of learners as they complete the project based learning activity. However, there are three key issues that require further research if we are to reap the teaching and learning benefits afforded by this educational approach: firstly, appropriate algorithms for use with the data captured by SAMs must be developed in a manner that ensures that the results from the application of these algorithms provides information that is useful to teachers and learners; secondly a visualisation must be developed of the on-going results of the data analysis that is suitable for use by teachers to support the formative assessment process and their provision of support to learners; and thirdly a visualisation for learners must also be developed to illustrate to them their progress and support their development of metacognitive awareness.

Aims

The overarching aims of the PhD is to investigate the design and visualisation of data analysis techniques to expose the learning processes in project based learning scenarios in a manner that supports both teachers and learners.

Objectives

The objectives of the PhD are:

  1. To use SAM technology to capture test data from project based learning activities completed by secondary school STEM students. Using this test data set to explore patterns of interactions and their relationship to the teaching and learning process;
  2. Discuss and retrieve the specific data collected with the SAM Labs team, to increase the “insight potential”.
  3. Define the “click-based tracking” strategy, or other, to be implemented or improved in the SAM App.
  4. Using the analysed test data set, to develop algorithms to enable future analysis of data captured during project based STEM learning activities;
  5. Develop, in collaboration with SAM Labs’ data science team, the benchmarking and algorithm training around SAM’s historical data.
  6. To develop visualisations for teachers and learners that will enable them to understand and improve the learning process;
  7. To work closely with teachers and learners in a participatory design process for both the algorithms and data visualisations.

Methodology

The methodology proposed will be participatory, iterative and cyclical. It will interweave the theoretical, empirical, design, development and evaluation aspects of the project. Participants will be drawn from secondary schools with whom we have an existing working relationship and will consist of teachers and learners. Empirical study design will adopt a case study approach, with each group of learners and their teacher forming a single case. Data analysis will be both quantitative for the data captured by the SAMs and qualitative for the data captured through workshops, interviews and observations. We will work with the company behind SAM labs to ensure that any technical issues are easily addressed.

Significance

The current state of the art with respect to learning analytics is focussed on constrained learning activities, for example through the use of intelligent tutoring systems or MOOCs. The complexity and open-ended nature of Project based learning presents a huge challenge that has not as yet been addressed by the Learning analytics community. The work completed through this PhD would shed light on this unexposed area and would be of great interest to the academic community. However, the significance of the proposed work is not limited to the intellectual, there are real benefits to be offered to teachers who employ Project based approaches, learners who take part in them, and SAM users. Such algorithms could eventually be employed by the SAM software to provide additional features to those interested in exploiting the learning capabilities of SAM.

Furthermore, the combination of qualitative and quantitative data of this research will be most insightful and beneficial in the development of SAM for learning. The proposed work will provide both groups with information that can positively support their successful progress and learning.

Timescale

The PhD should last 3 years, full-time.

Ethics

The data collection and evaluation activities will require ethical approval. SAM labs is registered with the Information Commissioner's Office and adheres to the Data Protection Act. We will work closely with schools and teachers to seek informed consent prior to each of the activities and will seek ethical approval from the outset using the UCL IOE process.

Outcomes

The outcomes of this research will be theoretical, methodological and technological. They will of course be specific to the questions designed by the student.  In general terms however they will include:

  1. An increased understanding of the teaching and learning process in Project based scenarios. A theoretical contribution to the literature about Project based learning will be made;
  2. A methodology for working with teachers and learners to explore the data capture, analysis and representation when using technology to support Project based STEM education;
  3. A framework for the collation and analysis of data captured during Project based learning activities using technology;
  4. Data visualisations to support teachers and learners to become more effective in Project based learning activities.

Plans for dissemination

The usual channels for academic dissemination will be used with papers presented at the Annual Science Education conference and Educational Data Analytics conference, and published in highly rated journals such as Science Education and Journal of Computer Assisted Learning.

Non-academic dissemination will be conducted through our work in schools and through workshops and seminars aimed at educators and policy makers.

Our collaboration with SAM labs will also offer a route for dissemination; in particular to the technical develop community who design technologies to support learning and teaching.

Candidate Requirements

Candidates must have a first class or upper second undergraduate degree or a Master’s degree in a relevant scientific discipline, including but not limited to computer science, engineering, psychology or education (preferably with a background in quantitative analysis).

Candidates must have the computer programming skills and experience necessary for undertaking the programming aspects of the research (data collation, data analysis, algorithm development, development of data visualisations for teachers and learners) e.g. using Java, Python, R or similar.

School teaching experience would be an advantage.

Key Refereneces

Poulovassilis, Alexandra, Sergio Gutierrez-Santos, and Manolis Mavrikis. Graph-based modelling of students’ interaction data from exploratory learning environments. Proceedings of the Second International Workshop on Graph-Based Educational Data Mining (GEDM 2015). CEUR-WS. 2015.

Mavrikis, Manolis, Zheng Zhu, Sergio Gutiérrez Santos, Alexandra Poulovassilis. Visualisation and Analysis of Students' Interaction Data in Exploratory earning Environments. Proceedings of the 24th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 2015.

Geraniou, E., Mavrikis, M., Gutierrez-Santos, S., & Poulovassilis, A. Teacher Assistance Tools for the Constructionist Classroom. Proc. Constructionism 2012, Athens, August 21-24, 2012.

Gutierrez-Santos, S., Geraniou, E., Pearce-Lazard, D., & Poulovassilis, A. (2012). Design of teacher assistance tools in an exploratory learning environment for algebraic generalization. Learning Technologies, IEEE Transactions on, 5(4), 366-376.

Noss, R., Poulovassilis, A., Geraniou, E., Gutierrez-Santos, S., Hoyles, C., Kahn, K., Magoulas, G.D., & Mavrikis, M. (2012). The design of a system to support exploratory learning of algebraic generalisation. Computers & Education, 59(1), 63-81, 2012

Further details about this project can be obtained from:

Principle Supervisor: Professor Rosemary Luckin (r.luckin@ioe.ac.uk)

http://www.ioe.ac.uk/staff/LKLB_30.html

Co-Supervisor: Professor Alex Poulovassilis (ap@dcs.bbk.ac.uk)

http://www.dcs.bbk.ac.uk/~ap/

Further information about PhDs at UCL Institute of Education and Birkbec is availabel from:

http://www.ioe.ac.uk/research/departments/phd/746.html

http://www.bbk.ac.uk/prospective/research

Application forms and details about how to apply are available from:

Please do NOT apply for this scholarship via UCL SELECT. The Bloomsbury application form is available from Isabelle Jerome: i.jerome@ioe.ac.uk

  • Isabelle Jerome, Bloomsbury DTC Administrator, Doctoral School, Institute of Education i.jerome@ioe.ac.uk

The required supporting documentation is as follows:

  1. A personal statement
  2. Covering letter and CV
  3. Transcripts of your undergraduate and (where applicable) postgraduate qualifications
  4. Two confidential references

Closing date for application is:

Friday 22 April 2016