Automated student engagement analytics: a short cut to transition monitoring?

Steven John Tucker

Research output: Contribution to conferencePoster

2 Downloads (Pure)

Abstract

The current higher education climate involves substantially larger class sizes, greater diversity amongst the cohort and a greater flexibility in curricular choice. As a result, monitoring student engagement and successful transition into HE or at key transition points within chosen programmes has become considerably more challenging. Furthermore, engagement (or lack of engagement) within a chosen subject area can be used as a predictive index of students who may be having difficultly and who are at increased risk of academic failure or leaving the University without a successful outcome. As part of the wider field of learning analytics, such measures can be used predictively to identify at risk students such that early intervention can be targeted to those that need it most, to help their retention, transition and progression within their degree programme. Therefore, effective engagement monitoring drives tailored and targeted support, maximising resource utilisation and sustainability, all of which are fundamentally important within the current HE business model. However, monitoring has traditionally been a manual, laborious and ineffective process and this is a major barrier to its accuracy and efficiency. The present poster will describe the use of Blackboard’s “Retention Centre” within the University of Aberdeen virtual learning environment (MyAberdeen) as a means of enhancing the efficacy of this process. The functionality allows flexible and tailored rules to be customised within course areas to alert coordinators in the event that students are failing to meet specific engagement criteria. Such parameters include last access of the virtual learning environment, adherence to deadlines, completion of selected tasks and actual grades received. The flexibility of the system allows coordinators to add identified students to a watch list, email them directly or take other intervention steps as necessary. The level of automation is entirely flexible, with the option to send editable automatic emails to students who don’t meet the rule criteria, or perhaps when 2 parameters are not met; alternatively, only the coordinator can be alerted, allowing a more personal approach. Indeed, the system can also be used to reinforce positive engagement by simple adjustment or addition of rules. This particular use of the system was seen as very positive by students. A variety of courses, at a variety of levels have been piloted using this automated monitoring instrument, and the specific findings of these will also be covered in the poster.
Original languageEnglish
Publication statusPublished - 2015
Event12th Enhancement Themes Conference - Crowne Plaza Hotel, Glasgow, United Kingdom
Duration: 9 Jun 201511 Jun 2015

Conference

Conference12th Enhancement Themes Conference
CountryUnited Kingdom
CityGlasgow
Period9/06/1511/06/15

Fingerprint

monitoring
student
poster
learning environment
flexibility
automation
functionality
utilization
sustainability
climate
efficiency
event
lack
present
resources
learning
education

Cite this

Tucker, S. J. (2015). Automated student engagement analytics: a short cut to transition monitoring?. Poster session presented at 12th Enhancement Themes Conference, Glasgow, United Kingdom.

Automated student engagement analytics : a short cut to transition monitoring? / Tucker, Steven John.

2015. Poster session presented at 12th Enhancement Themes Conference, Glasgow, United Kingdom.

Research output: Contribution to conferencePoster

Tucker, SJ 2015, 'Automated student engagement analytics: a short cut to transition monitoring?' 12th Enhancement Themes Conference, Glasgow, United Kingdom, 9/06/15 - 11/06/15, .
Tucker SJ. Automated student engagement analytics: a short cut to transition monitoring?. 2015. Poster session presented at 12th Enhancement Themes Conference, Glasgow, United Kingdom.
Tucker, Steven John. / Automated student engagement analytics : a short cut to transition monitoring?. Poster session presented at 12th Enhancement Themes Conference, Glasgow, United Kingdom.
@conference{fb78664c13e14288a1c68946d4a727de,
title = "Automated student engagement analytics: a short cut to transition monitoring?",
abstract = "The current higher education climate involves substantially larger class sizes, greater diversity amongst the cohort and a greater flexibility in curricular choice. As a result, monitoring student engagement and successful transition into HE or at key transition points within chosen programmes has become considerably more challenging. Furthermore, engagement (or lack of engagement) within a chosen subject area can be used as a predictive index of students who may be having difficultly and who are at increased risk of academic failure or leaving the University without a successful outcome. As part of the wider field of learning analytics, such measures can be used predictively to identify at risk students such that early intervention can be targeted to those that need it most, to help their retention, transition and progression within their degree programme. Therefore, effective engagement monitoring drives tailored and targeted support, maximising resource utilisation and sustainability, all of which are fundamentally important within the current HE business model. However, monitoring has traditionally been a manual, laborious and ineffective process and this is a major barrier to its accuracy and efficiency. The present poster will describe the use of Blackboard’s “Retention Centre” within the University of Aberdeen virtual learning environment (MyAberdeen) as a means of enhancing the efficacy of this process. The functionality allows flexible and tailored rules to be customised within course areas to alert coordinators in the event that students are failing to meet specific engagement criteria. Such parameters include last access of the virtual learning environment, adherence to deadlines, completion of selected tasks and actual grades received. The flexibility of the system allows coordinators to add identified students to a watch list, email them directly or take other intervention steps as necessary. The level of automation is entirely flexible, with the option to send editable automatic emails to students who don’t meet the rule criteria, or perhaps when 2 parameters are not met; alternatively, only the coordinator can be alerted, allowing a more personal approach. Indeed, the system can also be used to reinforce positive engagement by simple adjustment or addition of rules. This particular use of the system was seen as very positive by students. A variety of courses, at a variety of levels have been piloted using this automated monitoring instrument, and the specific findings of these will also be covered in the poster.",
author = "Tucker, {Steven John}",
year = "2015",
language = "English",
note = "12th Enhancement Themes Conference ; Conference date: 09-06-2015 Through 11-06-2015",

}

TY - CONF

T1 - Automated student engagement analytics

T2 - a short cut to transition monitoring?

AU - Tucker, Steven John

PY - 2015

Y1 - 2015

N2 - The current higher education climate involves substantially larger class sizes, greater diversity amongst the cohort and a greater flexibility in curricular choice. As a result, monitoring student engagement and successful transition into HE or at key transition points within chosen programmes has become considerably more challenging. Furthermore, engagement (or lack of engagement) within a chosen subject area can be used as a predictive index of students who may be having difficultly and who are at increased risk of academic failure or leaving the University without a successful outcome. As part of the wider field of learning analytics, such measures can be used predictively to identify at risk students such that early intervention can be targeted to those that need it most, to help their retention, transition and progression within their degree programme. Therefore, effective engagement monitoring drives tailored and targeted support, maximising resource utilisation and sustainability, all of which are fundamentally important within the current HE business model. However, monitoring has traditionally been a manual, laborious and ineffective process and this is a major barrier to its accuracy and efficiency. The present poster will describe the use of Blackboard’s “Retention Centre” within the University of Aberdeen virtual learning environment (MyAberdeen) as a means of enhancing the efficacy of this process. The functionality allows flexible and tailored rules to be customised within course areas to alert coordinators in the event that students are failing to meet specific engagement criteria. Such parameters include last access of the virtual learning environment, adherence to deadlines, completion of selected tasks and actual grades received. The flexibility of the system allows coordinators to add identified students to a watch list, email them directly or take other intervention steps as necessary. The level of automation is entirely flexible, with the option to send editable automatic emails to students who don’t meet the rule criteria, or perhaps when 2 parameters are not met; alternatively, only the coordinator can be alerted, allowing a more personal approach. Indeed, the system can also be used to reinforce positive engagement by simple adjustment or addition of rules. This particular use of the system was seen as very positive by students. A variety of courses, at a variety of levels have been piloted using this automated monitoring instrument, and the specific findings of these will also be covered in the poster.

AB - The current higher education climate involves substantially larger class sizes, greater diversity amongst the cohort and a greater flexibility in curricular choice. As a result, monitoring student engagement and successful transition into HE or at key transition points within chosen programmes has become considerably more challenging. Furthermore, engagement (or lack of engagement) within a chosen subject area can be used as a predictive index of students who may be having difficultly and who are at increased risk of academic failure or leaving the University without a successful outcome. As part of the wider field of learning analytics, such measures can be used predictively to identify at risk students such that early intervention can be targeted to those that need it most, to help their retention, transition and progression within their degree programme. Therefore, effective engagement monitoring drives tailored and targeted support, maximising resource utilisation and sustainability, all of which are fundamentally important within the current HE business model. However, monitoring has traditionally been a manual, laborious and ineffective process and this is a major barrier to its accuracy and efficiency. The present poster will describe the use of Blackboard’s “Retention Centre” within the University of Aberdeen virtual learning environment (MyAberdeen) as a means of enhancing the efficacy of this process. The functionality allows flexible and tailored rules to be customised within course areas to alert coordinators in the event that students are failing to meet specific engagement criteria. Such parameters include last access of the virtual learning environment, adherence to deadlines, completion of selected tasks and actual grades received. The flexibility of the system allows coordinators to add identified students to a watch list, email them directly or take other intervention steps as necessary. The level of automation is entirely flexible, with the option to send editable automatic emails to students who don’t meet the rule criteria, or perhaps when 2 parameters are not met; alternatively, only the coordinator can be alerted, allowing a more personal approach. Indeed, the system can also be used to reinforce positive engagement by simple adjustment or addition of rules. This particular use of the system was seen as very positive by students. A variety of courses, at a variety of levels have been piloted using this automated monitoring instrument, and the specific findings of these will also be covered in the poster.

M3 - Poster

ER -