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PeerWise – Experiences at University College London

September 13, 2013 in Uncategorized, Use cases

PeerWise – Experiences
at University College London

by Sam Green and Kevin Tang

Department of Linguistics, UCL

Introduction

In February 2012, as part of a small interdisciplinary team, wPeerWise_Logoe secured a small grant of
 £2500 from the Teaching Innovation Grant fund to develop and implement the use of PeerWise within a single module in the Department of Linguistics at University College London (UCL). The team was made up of various advisory staff from the Centre for Applied Learning and Teaching, also from the Division of Psychology and Language Sciences (PALS), and lecturers and Post-Graduate Teaching Assistants (PGTAs) in the department of Linguistics. The use of the system was monitored and student’s participation made up 10% of their grade for the term.

The subsequent academic year we extended its use across several further modules in the department by obtaining an e-Learning Development Grant at UCL.

Overall aims and objectives

The PGTAs adapted the material developed in the second half of the 2011/12 term to provide guidelines, training, and further support to new PGTAs and academic staff running modules using PeerWise. The experienced PGTAs were also be involved in disseminating the project outcome and sharing good practice.

Methodology – Explanation of what was done and why

Introductory session with PGTAs:

A session run by the experienced PGTAs was held prior to the start of term for PGTAs teaching on modules utilising PeerWise. This delivered information on the structure and technical aspects of the system, the implementation of the system in their module, and importantly marks and grading. This also highlighted the importance of team-work and necessity of participation. An introductory pack was provided for new PGTAs to quickly adapt the system for their respective modules.

Introductory session with students:

Students taking modules with a PeerWise component were required to attend a two-hour training and practice workshop, run by the PGTAs teaching on their module. After being given log-in instructions, students participated in the test environment set up by the PGTAs. These test environments contained a range of sample questions (written by the PGTAs) relating to students’ modules and which demonstrated to the students the quality of questions and level of difficulty required. More generally, students were given instructions on how to provide useful feedback, and how to create educational questions.

Our PGTA - Thanasis Soultatis giving an introductory session to PeerWise for students

Our PGTA – Thanasis Soultatis giving an introductory session to PeerWise for students

Our PGTA - Kevin Tang giving an introductory session to PeerWise for students

Our PGTA – Thanasis Soultatis giving an introductory session to PeerWise for students

Course integration

In the pilot implementation of PeerWise, BA but not MA students were required to participate. BA students showed more participation than MA students, but the latter nevertheless showed engagement with the system. Therefore, it was decided to make PeerWise a compulsory element of the module to maximise the efficacy of peer-learning.

It was decided that students should work in ‘mixed ability’ groups, due to the difficult nature of creating questions. However, to effectively monitor individual performance, questions were required to be answered individually. Deadlines situated throughout the course ensured that students engaged with that week’s material, and spread out the workload.

Technical improvement

The restriction of image size and lack of an ability to upload or embed audio files (useful for phonetic/phonological questions in Linguistics) was circumvented by using a UCL-wide system which allows students to host these sorts of files. This system (MyPortfolio) allows users to create links to stored media. This also allows the students to effectively anonymise the files, thus keeping them secret for the purpose of questioning.

Project outcomes

Using the PeerWise administration tools, we observed student participation over time. Students met question creation deadlines as required, mostly by working throughout the week to complete the weekly task. In addition, questions were answered throughout the week, revealing that students didn’t appear to see the task purely as a chore. Further, most students answered more than the required number of questions, again showing their willing engagement. The final point on deadlines was that MA students used PeerWise as a revision tool entirely by their own choices. Their regular creation of questions created a repository of revision topics with questions, answers, and explanations

Active Engagement

Active Engagement

The Statistics

PeerWise provides a set of PeerWise scores. To increase the total score, one needs to achieve good scores for each component.

The students were required to:

  • write relevant, high-quality questions with well thought-out alternatives and clear explanations
  • answer questions
  • rate questions and leave constructive feedback
  • use PeerWise early (after questions are made available) as the score increases over time based on the contribution history

Correlations between the PeerWise scores and the module scores were performed to test the effectiveness of PeerWise on student’s learning. A nested model comparison was performed to test the effectiveness of the PeerWise grouping in prediction of the students’ performance. The performance in Term 1 differs somewhat between the BA students and MA students, but not in Term 2 after manipulations with the PeerWise grouping with the BAs.

Term 1:

The BA students showed no correlation at all, while the MAs showed a strong correlation (r = 0.49, p < 0.001***)

MA Students - Term 1 - Correlation between PeerWise Scores and Exam Scores

MA Students – Term 1 – Correlation between PeerWise Scores and Exam Scores

In light of this finding, we attempted to identify the reasons behind this divergence in correlations. One potential reason was that grouping with the BAs was done randomly, rather than by mixed-ability, while the grouping with the MAs was done by mixed-ability. We,  hypothesized that mixed-ability grouping is essential to the successful use of the system. To test this hypothesis, we asked the PGTA for the BAs to regroup the PeerWise groups in the second term based on mixed-ability. This PGTA did not have any knowledge of the students’ Peerwise scores in Term 1, while the PeerWise grouping for the MAs largely remained the same.

Term 2:

The assignments in Term 2 were based on three assignments spread out over the term. The final PeerWise score (taken at the end of the Term 2) was tested for correlation with each of the three assignments.

With the BAs, the PeerWise score correlated with all three assignments with increasing levels of statistical significance – Assignment 1 (r = 0.44, p = 0.0069**), Assignment 2 (r = 0.47, p = .0.0040*) and Assignment 3 (r = 0.47, p = .0.0035**).

With the MAs, the findings were similar, with the difference that Assignment 1 was not significant with a borderline p-value of 0.0513 – Assignment 1 (r = 0.28, p = 0.0513), Assignment 2 (r = 0.46, p = 0.0026**) and Assignment 3 (r = 0.33, p = 0.0251**).

A further analysis was performed to test if PeerWise grouping has an effect on assignment performance. This consisted of a nested-model comparison with PeerWise score and PeerWise Group as predictors, and the mean assignment scores as the predictee. The lm function in R statistical package was used to build two models, the superset model having both PeerWise score and PeerWise Group as the predictors, and the subset model having only the PeerWise score as the predictor. An ANOVA was used to compare the two models, and it was found that while both PeerWise scores and PeerWise grouping were significant predictors separately, PeerWise grouping made a significant improvement in prediction with p < 0.05 * (see Table 1 for the nested-model output).

Table 1: ANOVA results

Analysis of Variance Table

Model 1: Assignment_Mean ~ PW_score + group
Model 2: Assignment_Mean ~ PW_score
  Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
1     28 2102.1                              
2     29 2460.3 -1   -358.21 4.7713 0.03747 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

The strong correlation found with the BA group in Term 2 (but not in Term 1) is likely to be due to the introduction of mixed-ability grouping. The group effect suggests that the students performed at a similar level as a group, which implies group learning. This effect was only found with the BAs but not with the Mas; this difference could be attributed to the quality of the mixed-ability grouping since the BAs (re)grouping was based on Term 1 performance, while the MA grouping was based on the impression on the students that the TA had in the first two weeks of Term 1. With the BAs and MAs, there was a small increase of correlation and significance level over the term; this might suggest that the increasing use of the system assists with improving assignment grades over the term.

Together these findings suggest that mixed-ability grouping is key to peer learning.

Evaluation/reflection:

A questionnaire was completed by the students about their experience with our implementation of PeerWise. The feedback was on the whole positive with a majority of students agreeing that

  1. Developing original questions on course topics improved their understanding of those topics
  2. Answering questions written by other students improved their understanding of those topics.
  3. Their groups worked well together

These highlighted the key concept of PeerWise – Peer Learning

feedback_understandingfeedback_writtenfeedback_group

Our objective statistical analyses together with the subjective feedback from the students themselves strongly indicated that the project enhanced student learning and benefitted their learning experience.

E-learning awareness

One important experience was the recognition that peer learning – using e-learning – can be a highly effective method of learning for students, even with low amounts of any regular and direct contact from PGTAs to students regarding their participation.

It was necessary to be considerate of the aims of the modules, understand the capabilities of PeerWise and it’s potential for integration with the module, and importantly to plan in detail the whole module’s use of PeerWise from the beginning. Initiating this type of e-learning system required this investigation and planning in order for students to understand the requirements and the relationship of the system to their module. Without explicit prior planning, with teams working in groups and remotely from PGTAs and staff (at least, with regards their PeerWise interaction), any serious issues with the system and its use may not have been spotted and/or may have been difficult to counteract.

As mentioned, the remote nature of the work meant that students might not readily inform PGTAs of issues they may have been having, so any small comment was dealt with immediately. One issue that arose was group members’ cooperation; this required swift and definitive action, which was then communicated to all relevant parties. In particular, any misunderstandings with the requirements were dealt with quickly, with e-mails sent out to all students, even if only one individual or group expressed concern or misunderstanding.

Dissemination and continuation

A division-wide talk (video recorded as a ‘lecturecast’) was given by Kevin Tang and Sam Green (the original PGTAs working with PeerWise) introducing the system to staff within the Division of Psychology and Language Sciences. This advertised the use and success of PeerWise to several interested parties, as did a subsequent lunchtime talk to staff at the Centre for the Advancement of Teaching and Learning. As the experienced PGTAs documented their experiences in detail, created a comprehensive user-guide, included presentations for students and new administrators of PeerWise, and made this readily-available for UCL staff and PGTAs, the system can capably be taken up by any other department. Further, within the Department of Linguistics there are several ‘second-generation’ PGTAs who have learned the details of, and used, PeerWise for their modules. These PGTAs will in turn pass on use of the system to the subsequent year, should PeerWise be used again; they will also be available to assist any new users of the system. In sum, given the detailed information available, and current use of the system by the Department of Linguistics, as well as the keen use by staff in the department (especially given the positive results of its uptake), it seems highly likely that PeerWise will continue to be used by several modules, and will likely be taken up by others.

Acknowledgements