4.2.3 Electronic Voting Systems

Electronic Voting Systems

Electronic Voting Systems (EVS) are a means through which a presenter can conduct an electronic poll or survey and generate histograms for an aggregated view of responses. Within Higher Education, Electronic Voting Systems are often utilised to improve or augment in-class engagement.

During a live lecture, it may prove to be challenging to readily ascertain whether students understand a taught concept, or feel confident enough in their ability to apply them. With one traditional method – conducting a ‘straw poll’, or asking for a ‘show of hands’ – students who do not understand the question may choose to abstain rather than to appear incorrect, or may base their own response on that of the visible majority in the room.

Indeed, Beatty (2004) concludes that:

  • Students are often ‘more afraid of being incoherent than incorrect’ when interacting with an instructor in the presence of their peers.
  • As such, students ‘may want to know their peers opinions but may feel reluctant to share their own’
  • Students are often most hesitant to ask questions in class when they do not understand the material
  • Some students prefer classes without ‘traditional participation’

It is in these situations where an Electronic Voting System may be deployed as a means to ‘empower’ rather than ‘enforce’ participation in-class (Graham, C. R. et al. 2007) due to its capacity to facilitate anonymous responses.

Physical Hardware or ‘Bring Your Own Device’

In the past, Electronic Voting Systems solely relied upon the provision of voting hardware being disseminated in order for participants to tender responses (see In-Class Clickers). With the advent of smartphones, tablets and handheld devices, modern electronic voting systems are often software, as opposed to hardware dependent. This means that participants may use their own devices to respond to polls – which may be termed as a ‘Bring Your Own Device’ (BYOD) model of polling. The University’s supported EVS tool (Mentimeter) uses a BYOD model.

Session design with in-class polling

Peer instruction

Peer instruction (Mazur, 1997) is a model for stimulating discussion and learning in face-to-face teaching sessions. Eric Mazur argues that the greatest value in the live lecture is the intrinsic potential for spontaneous interaction – but that the social etiquette around learning encounters (and even the architecture of some lecture theaters) may tacitly encourage passive participation.

As such, the Peer Instruction champions the idea that students who have recently solved a given problem may be well-placed to explain the thought processes involved in finding their solution to their peers. Therefore, by presenting a problem and asking students to discuss their answers with each other before providing the correct solution, an instructor can encourage the process of ‘externalising’ answers to shift the pedagogical focus of such engagement exercises from surface fact retention to reasoning.

The method can also be used to challenge students preconceptions through asking them to justify their response to questions with their peers. This can result in a change of thinking, but is supported by the use of pre- and post- discussion polling to show the lecturer if students have understood an underlying principle. The lecturer can then target subsequent content – perhaps spending more time on aspects the cohort has found difficult, rather than having to guess what students are understanding.

The video below (available to UoY users) is a capture of Professor Simon Lancaster’s lunchtime workshop on using in-class polling to stimulate learning through peer instruction activities. One of the significant points is to generate questions in class that force students to think, rather than just recall facts (blog post for this session).Watch Video: Simon Lancaster ResponseWare Lunch and Learn (York Users Only)

One approach to expand on the question-element of a peer instruction workflow is to build in a reflective post-question step to encourage students to meta-cogitate, and become more proficient self-assessors. By using a consistent approach to do this, it is possible to facilitate the longitudinal capture and subsequent scrutinising of response data. This allows an educator to accurately demonstrate learning gain, and also to reflect upon the effectiveness of their own teaching methods, thus also serving as a lens onto their own practice. You can read more about this approach in a blog post which covers a workshop delivered by Dr. Fabio Arico.

Case studies

Emma Rand, ResponseWare

Using Electronic Voting Systems to Engage Large Cohorts

Emma Rand, Biology
View Case Study

Jess Wardman, The York Management School

View Case Study

Crowd-sourcing answers

Not all questions can be answered with a multiple-choice response. Text-based responses can be gathered using most ‘BYOD’-based Electronic Voting Systems, and displayed in either a ‘WordCloud’ or a Wall of Text. This approach may be useful to collect ideas from the group which can then be used to stimulate further discussion or generate a collective response to a case study situation.

Case studies

Victoria Jack, CELT

Crowd-Sourcing Responses using MentiMeter

Victoria Jack, CELT
View Case Study

Selected Literature