what are we learning from learning analytics: rhetoric to reality escalate 2014

What are we learning from

learning analytics?

Shane [email protected]

Twitter: @shaned07

• Student from Shanghai-based East China Normal University

• "Last month, you spent less on meals. Are you in financial difficulty? If so, please contact me via phone, text message or e-mail.“

Introduction

http://www.bjreview.com.cn/nation/txt/2014-06/23/content_625466.htm

• Automatically track students' meal card spending.

• If spending falls under a threshold level, a designated faculty member sends the student a short message to check whether they are in financial difficulty.

Introduction

http://www.bjreview.com.cn/nation/txt/2014-06/23/content_625466.htm

• Highlights the rapidly growing list of applications of student data

• Academic

• Social

• Pastoral

Introduction

This talk:

• What are we learning from the implementation of LA into HE?

• What are the conversations, expectations and reactions to this nascent field?

• What are the emerging models for institutional implementation?

Introduction

Does the rhetoric of LA meet the reality?

Introduction

• Why the interest in LA now?

Drivers

• 1926 - Pressey built an instructional machine to provide multiple choice questions

• “…with the addition of a simple attachment the apparatus will present the subject with a piece of candy or other reward upon his making on any given score for which the experimenter may have set the device…”

Drivers

Shute, V. J., & Psotka, J. (1994). Intelligent Tutoring Systems: Past, Present, and Future (No. AL/HR-TP-1994-0005). ARMSTRONG LAB BROOKS AFB TX HUMAN RESOURCES DIRECTORATE.

• Scale, access and application• Ease of access to learner data – LMS, SIS, mobile• Growth in adoption of technical devices• Huge investment in analytics – industry &

Government

Data

• Learning Analytics • “game changer” for education

Learning Analytics

…is the collection, collation, analysis and reportingof data about learners and their contexts, for the purposes of understanding and optimizing learning

• One perspective - Industry

Industry rhetoric

Industry rhetoric

“Get answers to your most important questions like:• How can I easily find students who are at-risk?

Industry rhetoric

“Get answers to your most important questions like:• How can I easily find students who are at-risk?

• Yes possible – much research in this area • However, ignores the complexity• Context is critical• Not all courses are alike – student diversity

and approach

Overstated

Industry rhetoric

“Get answers to your most important questions like:• Who are the most innovative instructors?”

Industry rhetoric

“Get answers to your most important questions like:• Who are the most innovative instructors?”

• How and why? What defines innovative in this space given the myriad of tools and learning approaches available

Why?

Industry rhetoric

“…In five years the classroom will learn you! And personalize course work accordingly”

http://www.research.ibm.com/cognitive-computing/machine-learning-applications/decision-support-

education.shtml#fbid=MRUeQg4jzVG

Industry rhetoric

“…In five years the classroom will learn you! And personalize course work accordingly”

• Currently available if:• Cognitive tutor, Knewton, Knowillage

• Ryan Baker – on/off task behaviour; gaming and choice of major

Plausible

Industry rhetoric

“Enhance student outcomes with the ability to monitor, evaluate, and predict learner performance to drive retention and improve outcomes.”

• Much work in this area to predict performance however, intervention strategies less well understood.

• Greater recognition SRL

http://www.brightspace.com/solutions/higher-education/advanced-analytics/

Available but not utilised

Industry rhetoric

“…predictive analytics capabilities help educators target learning strategies and pre-emptively mentor at-risk learners.”

http://www.brightspace.com/solutions/higher-education/advanced-analytics/https://www.flickr.com/photos/tadeeej/3228729514/

Industry rhetoric

Do we need predictive analytics here?

https://www.flickr.com/photos/tadeeej/3228729514/

Industry rhetoric

• Unlikely – practice is difficult change. However first step is to aid identification.

• Tannes et al (2011) - Course Signals feedback• Instructors – feedback was motivational• Student success related to instructional

feedback

Tannes, et al (2011) . Using Signals for appropriate feedback. Perceptions and practice. Computers and Education, 57, (4), 2414 - 2422

Industry offer solutions to problems

We still need to identify the problem.

Industry rhetoric

What is missing: a focus on learning process

• SRL proficiency (Gasevic; Winne)• Discourse analysis and text mining (Rose)• Learning design and Instructional conditions

(Lockyer; Gasevic)• Learning dispositions (Deakin Crick, Buckingham

Shum)• Literacies or fluencies (Siemens)• Creativity (Pei Ling Tan)

Research rhetoric

Great research BUT:

• Tends to ignore the complexity of university wide practice

• Predominantly, small scale and technology and institutional specific

• Lacks guidance to aid further adoption• Frequently requires high level skills and capacities

Research rhetoric

Hence:• Very few university wide examples of LA adoption

• But obviously an area of increasing need and importance

Research rhetoric

Leads to questions related to how to

implement, get started and what data?

National project to benchmark LA status, policy

and practices for Australian Universities

Learning Analytics

Interviews with 39 Universities and 30 “experts”:

• Identification of current practice, methods and approaches

• Identification of key drivers for institutions, stage of development, process for implementation, project leads

Benchmarking

Research perspective:

• Focus on understanding learning processes• Broad range of data sets –larger size and range

of data (relational data) • Limited interest in the scalability of findings

across institution (at least not a stated intention)

Benchmarking

Research perspective:

“My hope [for LA] is that we can develop a better theory about how people learn and forge recommendations that might nudge learners toward more productive, more efficient, more satisfying ways of learning”

Benchmarking

University leaders perspective:

• Primarily focused on retention• “It’s [LA] a tool for improving retention”• Limited mention of LA as a means to improve

learning• Main driver is budget (cost savings)• Perception that it is only related to – LMS and

SIS• Limited number of data sets considered

Benchmarking

University leaders perspective:

• Success is seen as staff access to information• Limited understanding of the application of

interventions that are data informed• Data visualisations – dashboard development is

the endpoint and goal• Few institutions with stated LA policy and strategy

Benchmarking

• Widening gap between University Admin and researchers

• Admin – Industry very similar

Benchmarking

Reality is sobering:

• Need to develop greater understanding of the role of technology and role of data in an institution

• Access to data does not mean change in practice

• Interventions and early alerts must be constantly evaluated, revised and contextualised

Reality

2005 – Goldstein & Katz:

• Stage 1: Extraction and reporting of transaction-level data

• Stage 2: Analysis and monitoring of operational performance

• Stage 3: “What-if” decision support (such as scenario building)

• Stage 4: Predictive modeling & simulation• Stage 5: Automatic triggers and alerts

(interventions)

Reality

• Yanosky (2009) – 305 institutions, 58% at stage 1, 20% at stage 2

• Bichsel (2012)• Interest in analytics is high, but many

institutions had yet to make progress beyond basic reporting.

2014 LA organisational adoption is low:

• Australia is predominantly at a stage of basic reporting

• Very few institutions have an enterprise approach

• While the research has well progressed -implementation remains a challenge.

Reality

• Essentially, 2 models emerging1. Solutions focused

• IT driven or• L&T driven or• Industry

2. Process focused• Individual “faculty” or• Networked and integrated

Reality

Reality

Adaptability of system to meet org needs

Low High

Low

High

Ease of adoption

Reality


Low High

Low

High

Ease of adoption

Solutions

focused

Process

focused

Reality


Low High

Low

High

Long term impact

Solutions

focused

Process

focused

Solutions focused – Short term gains

Reality

Advantages Disadvantages

• Cost • Locked in

• Speed of delivery • Short time for acceptance

• Ease of dissemination

• Lacks capacity building

• Scalable, risk mitigation

• Access to data is often limited

Process focused – Longer term gains

Reality

Advantages Disadvantages

• Capacity building • Time required

• Adaptive to changing reqs

• Sustained leadership and principles of access

• Acceptance ofprocess

• Complexity

• Shared ownership • Raises org threat

• Evidenced based

Common model – Solutions focused:

• IT lead and implemented• Closed system focused on scalability,

performance, and list of features • Dashboards/ reports are important• Dissemination and access gains

[Success is seen as staff access to information]

• Where is the why?

Reality

Conclusion

LA sophistication modelSiemens, G., Dawson, S., & Lynch, G. (2013). Improving the Productivity of the Higher Education Sector: Policy and Strategy for Systems-Level Deployment of Learning Analytics. Society for Learning Analytics Research for the Australian Government Office for Learning and Teaching.

Conclusion

Conclusion

Solutions focusedLimited view of LA – eg retention

Is there an alternative:

• What are the organisational needs and how to gain both impact and adoption

• How do we merge both models to gain both short and long term impact?

Reality

Developing models:

• Cross organisation• IT, L&T, Faculty, Research, Administrators

• Development of exemplars and research informed.

• Process is future looking and agile• Increased time required for acceptance and

discussion• Problem focused – understand the problem

An alternative

Developing models:

• Building organisational capacity• Time for organisational acceptance• Identify sites of interest and growth• Research ideas promoted and faculty invited

into new spaces• Need to act on data and findings

An alternative

Complex adaptive system:

• Education is complex• Learning is complex• Organisations are complex

• CAS are systems large numbers of agents that interact and adapt or learn

• Non-linear and resilient

Complex Leadership Theory:

• CAS – requires new forms of leadership (Complex leadership theory - Uhl-Bien et al)

• Interactive, engaged, multi-level and contextual

• Takes advantage of the dynamic capabilities the system

• Leadership vs leaders

Uhl-Bien, M., Marion, R. & McKelvey, B. (2007). Complexity Leadership Theory: Shifting leadership from the industrial age to the knowledge era, The Leadership Quarterly, Volume 18(4),298-318

Complexity Leadership:

Administrative Leadership

Adaptive Leadership



Adaptive Leadership

Administrative stifles

adaptive. (Bureaucratic

and top down)

However – it is driven

and solution focused



Adaptive Leadership

Adaptive (lack of

integration)

However capacity

building and

innovation focused



Adaptive Leadership

Balanced

Capacity building,

innovative

responses to

complex problems

Enabling

Enabling:

• Leadership- focused on process and enabling staff

• Developing awareness and building capacity

• Diverse teams represented

• IT/ L&T – systems

• Data analysts

• Data wranglers

• Teaching staff

• ResearchersE.g.

• Open UK

• University of Michigan

• University of Texas

Conclusion

Process focusedBroad view of LA

Conclusion

• Change in education is complex and multi-faceted• Requires new models for implementation and

leadership• Enabling leadership• models that are agile and research informed

• Requires an inter-disciplinary approach• Embrace Friction - generates discussion and

innovation

Conclusion

For the reality of LA to meet the rhetoric (to reach potential):

• LA is not a technology

• LA is not a dashboard

• LA is not one individual

• LA is team based

• LA is dynamic and requires longer term

investment and process

Thank you…

Questions?

[email protected]

Twitter: @shaned07

what are we learning from learning analytics: rhetoric to reality escalate 2014

Data & Analytics