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Supporting students

Using knowledge from Associate Lecturers in a Bayesian model to predict the probability of students’ results

Project leader(s): 
Fadlalla Elfadaly
Faculty: 
STEM
Status: 
Current
Body: 

Predicting the passing probabilities of an online module has always been an interest in higher education. It helps in planning, decision making, providing feedback to students and also in helping students work around any difficulties or milestones that are predicted to affect their results. Different models have been built to predict the passing probability as a binary variable (pass/fail) based on a set of explanatory variables.

In distance learning, and specifically at The OU educational system, Associate lecturers (ALs) are the main contact point with their allocated students. Across successive presentations, ALs are expected to build their expertise about the modules they are teaching and also on the anticipated characteristics and abilities of their students. We believe that incorporating the ALs’ opinion and expertise together with the data can be highly beneficial in building predictive models and will markedly enhance the predicted probabilities.

Moreover, for more accurate planning and to tailor the feedback to the required level of each student, a better model should allow the prediction of probabilities for all possible pass grades (pass1, pass 2 and so on) instead of just (pass/fail).

The proposed project aims at building a Bayesian multinomial logistic model with more than two outcomes that allows for the experts’ knowledge (ALs in this case) to be quantified as a subjective prior distribution for the Bayesian analysis. To the best of our knowledge, ALs knowledge and expertise on students performance have never been implemented with the available data during the modelling stage of analysis. It is anticipated that including the ALs input in this way at the modelling stage will help predicting more accurate probabilities of students’ results.

We aim for a two-year project where the methods and tools are used in the first year to build the models, elicit the ALs’ opinion and predict the probabilities of different outcomes. This will provide a full prediction system that can be used for future presentations. In the second year of the project we aim to test and evaluate our proposed system. The predicted probabilities computed with ALs' input will be compared to those based only on the data and both will be compared to the actual module results. This should give a clear idea on the impact of incorporating ALs’ knowledge into modelling the analysis.

The proposed system will provide a tool that can be used in successive presentations of different modules, where ALs will be able to input their own knowledge and opinion on students performance to model the probabilities of passing the module. Based on these accurately predicted probabilities of different outcomes, both ALs and students will efficiently work together on the actual students needs according to their predicted level of competency in the module work. For example, student support can be efficiently tailored to individual students and hence improve students’ satisfaction and/or retention. For example, students with potential risk of failure can be identified to get more support. 

Fadlalla Elfadaly, Carol Calvert and Rachel Hilliam poster (PPT)

Welsh-medium tuition in Level 1 Mathematics/Addysgu Mathemateg Lefel 1 trwy gyfrwng y Gymraeg

Project leader(s): 
Andrew Potter, Delyth Tomos and Chris Hughes
Faculty: 
STEM
Status: 
Current
Body: 

Welsh-medium education has attracted increased attention over the past 50 years. Much research has been conducted in secondary and higher education contexts, including the teaching and learning of mathematics in Welsh. However, there is a gap in research which explores the topic of Welsh-medium mathematics tuition in a distance learning context. Moreover, there is a lack of consideration in the literature of the experiences of older adult (i.e. aged 25+) learners.

The research question for this project is: “What are the factors which affect student engagement in a bilingual Welsh/English mathematics distance-learning context?”

For many students, MU123 Discovering Mathematics will be the first OU module they study as part of a range of different study intentions. Some students approach MU123 with a degree of apprehension about their mathematical ability. We are particularly interested in exploring whether offering bilingual tutorial sessions, tutor support and a Welsh-language forum is a factor in student engagement on MU123.

It is anticipated that Welsh is the first language of some students, and that many may have had experience of Welsh-medium tuition at school. In addition, it is expected that some of the increasing number of Welsh learners across the UK may also be interested in Welsh-medium tuition as a means of improving their language proficiency and situating their language learning in an immersive, real-life context. This project will seek to explore the experiences of the broad range of Welsh competencies that we assume to exist amongst MU123 students. For this reason we have chosen a translanguaging approach to bilingual tuition (see Rationale), in order to allow speakers of all levels of fluency to participate.

It is expected that the findings of this project will help increase understanding of the factors surrounding student engagement in bilingual distance-learning contexts, on MU123 in future presentations, and for Level 1 study in STEM subjects more generally.

Andrew Potter, Delyth Tomos and Chris Hughes poster (PPT)

Support for Students. Teaching for Tutors. An Investigation into Ideas on Encouraging Students to Engage

Project leader(s): 
Cathryn Peoples
Faculty: 
STEM
Status: 
Current
Body: 

In 19J, the Project Lead carried out an investigation on providing personalised support to two groups of Level 3 Software Engineering students. The literature suggests that students want more attention from their tutors, a general greater level of support, and a sense of belonging [1] [2]. In practice, however, the findings differed from what is reported in the literature: A minority of students were interested in the personalised support in reality, and the students who engaged formed the higher performing cohort. The students who had the most to gain, the disadvantaged students, therefore did not receive personalised support beyond that which is typically provided. There was therefore a gap between performance achieved across the module 

The objective of this proposed investigation is to respond to this gap, by gaining an understanding of the reasons why certain students do not engage with their tutor and/or their study, and to provision mechanisms which might encourage their engagement. This will investigate the “Support for Students” aspect in the project title, in the sense of understanding if non-engagement is a result of student characteristics, ability, and/or personal circumstances. The second part of the investigation will examine the “Teaching for Tutors” angle, in an attempt to understand if the reasons why a particular cohort of students has not engaged is because they believe their tutor to be unapproachable. The target is then to deploy approaches, from the introductory contact from the tutor to the post-exam period, which respond to the needs of this cohort. The project will conclude with an assessment of the suitability of the proposed approaches in terms of the frequency, type and quality of student engagement.

The intention is that the project will be applicable to other modules. If it is identified that a more personalised approach to communicating with students becomes effective when initial communication attempts are not responded to, the introductory approaches and subsequent support approaches can be applied.

Cathryn Peoples project poster


[1] P. Humphreys, “The Top Five Things That Really Matter to Students about their University,” JISC, Mar. 2018.

[2] A. Mountford-Zimdars, D. Sabri, J. Moore, J. Sanders, S. Jones, and L. Higham, “Causes of Differences in Student Outcomes,” HEFCE, Jul. 2015.

 

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