Learning analytics - The Open University of Hong Kong of LA.pdf · [email protected] ... • Turnitin scores . ... Course Signals at Purdue: Using learning analytics to increase

Learning analytics

Collecting learning insights: Unpacking the complexity of

learning analytics

Shane Dawson [email protected] Twitter: @shaned07

Question

How familiar are you with the field of learning analytics? A. Never heard of it B. A little; have read some research C. Very familiar; follow the research D. Expert; undertake research and actively

contribute to the fields development

Overview

• Background and overview • Data and algorithms • Learning Analytics

• Technologies and data • Course grain to fine grain data • Exemplars and case studies • Complexity of LA

More things change the more they stay the same

http://www.pellinstitute.org/downloads/publications-Indicators_of_Higher_Education_Equity_in_the_US_45_Year_Trend_Report.pdf

High-income 8 times more likely than low-income to obtain a bachelor’s degree by age 24 In the US the gap is increasing: • 2013 – 8 times • 1970 – 6 times

http://www.pellinstitute.org/downloads/publications-Indicators_of_Higher_Education_Equity_in_the_US_45_Year_Trend_Report.pdf

• Student diversity increasing • Increased part time employment • Gender distribution changing across fields

• (Females under-represented in STEM; Males under-represented in Education, health, arts)

• Decreased HE funding and rising costs • Increased demand for University

http://theconversation.com/who-goes-to-university-the-changing-profile-of-our-students-40373

Minimal impact. Digital disruption?

Student feedback under utilised Assessment processes poor Lack of proficiency in self-regulated learning

Freeman, S., et.al. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

Big data and analytics

Massive interest in data and analytics.

• > 5 Billion mobile phones • > 1 Billion Youtube users • 300 hrs of video uploaded to YouTube/ minute • $600 drive to store all of the worlds music • 60% increase in operating margin for retailers using

big data

Its accessible, cheap and critical Manyika, J., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity: McKinsey Global Institute

Algorithms – predictions

Predictive modelling


Target coupons inform father of daughter's pregnancy


Analytics for everything

Education is no different • Huge investment in analytics • Ease of access to learner data - LMS • Growth in technical devices • Growth in blended/ online learning models

Education context

Examples in education

• Academic performance • Student retention • Pastoral care • Academic literacies • Social networks – collaborations

• 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.“

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

Education context

• 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.

Education context

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

What are learning analytics? • Academic analytics • Learning analytics • Institutional analytics • Educational data mining • Assessment analytics • Social learning analytics • Business intelligence

Learning Analytics

• Learning analytics is a bricolage field • Incorporates diverse methods and approaches:

– Statistics – Data mining – Machine Learning – Artificial Intelligence – Text Mining – Information Visualization – Social Network Analysis

Learning Analytics

Learning Analytics

Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5), 30.

Definition

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

Learning Analytics • “game changer” for education

Definition

Learning analytics …the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data

Cooper, 2012

Definition

Similarities in definitions: Use of learner/educational data to provide actionable insight to aid the learning process

Large data sets – trends/ patterns or anomalies.

Learning Analytics

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

Data environments Analysis methods for LA What tools? Learning Analytics cycle Worked example

Technologies and analyses



Data types

Data environments • Student information system (SIS) • Learning management system • Assessment • Survey data (e.g. MSLQ; COI; Grit) • Course evaluations • Other related learning technologies

• Publishers • Quizzes • Video

• Structured data – Data is stored in DB tables and highly organized. – e.g. Student name, grades, address

• Unstructured data

– Everything else - text, audio, images, video

Data types

Data environments Analysis methods for LA What tools? Learning Analytics cycle Worked example



Analysis methods See: “Big data and Education” Ryan Baker. http://www.columbia.edu/~rsb2162/bigdataeducation.html

Different data types - different analytical approaches.

Data analysis

• Unstructured data analysis – Natural Language Processing and text analytics – E.g. topic modelling; sentiment analysis – (people, places, locations, dates, times etc., sentiment

analysis (+,-) – Audio analysis – E.g. speaker identification, language identification,

speech to text – Video analysis – E.g. facial and object recognition

Data analysis

• Descriptive statistics: – sums, means, std devs, basic plotting (graphs, charts,

histograms) • Machine learning:

– find patterns in data to perform either classification, clustering or prediction

– e.g. decision trees, neural networks, support vector machines, linear regression, self organising maps, k-means

• Predictive analytics – – Algorithmic approaches (generally machine learning)

for predicting key target variables of interest.

Data analysis


Common approaches – structured data • Prediction • Structure discovery • Relationship mining

Baker, R., and Salvador Inventado, P. (2014). Educational data mining and learning analytics." In J. Larusson & B. White. Learning Analytics, pp. 61-75. Springer New York, 2014.


• Prediction

• Classification • Regression • Latent Knowledge Estimation

Models to “predict” outcome/variable based on predictor variables

Baker, R., and Salvador Inventado, P. (2014). Educational data mining and learning analytics." In J. Larusson & B. White. Learning Analytics, pp. 61-75. Springer New York, 2014.


Examples • Predict

• Which students will pass a course? • Which students will stay in the course?

• Can inform • Support interventions • Curriculum design, assessment, recruitment,

marketing, pre-requisites


• Structure discovery

• Clustering • Factor analysis • Network analysis

• Will explore network analysis later. Identify emergent patterns and structure in data No specific target or predictor


Analysis methods • Relationship mining

• Discover relationships between variables • Association Rule mining

• If/then statements - analyses and predicts behaviour

• Need to be careful interpreting

• Diapers and beer • Target example

Analysis methods for LA? What tools? Learning Analytics cycle What questions?



Tools • Weka

• http://www.cs.waikato.ac.nz/ml/weka/ • R

• www.r-project.org • Rapidminer

• https://rapidminer.com/ • SNA specific tools

• Gephi • Netdraw/ Ucinet • Pajek

Analysis methods for LA? What tools? Learning Analytics cycle Worked example


The learning analytics cycle


Clow, D. (2012). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134-138). ACM.


Student demographics FT/PT

Learning design

Engagement, time? Technology choice

Support processes Dashboards Sensemaking

Assessment models Retention/ Std success




Develop an early alert process for student retention and success


Data sources • Student Information System

• Entry pathway (HS; mature age) • Past subjects studied/ grades • International/ domestic • Parental education • Home address • Program of study • Course of study • Course load • PT/ FT


Data sources • Learning Management System

• Access • Forum postings, reads, replies • Quiz attempts and scores • Lecture capture • Downloads • Video • Assignment submissions • Turnitin scores


A simple model: • Queensland University of Technology (QUT) • Built off Vincent Tinto’s work – social and academic

integration • First Year Experience • Transition pedagogy



• All student first year data • Regression analysis – students that pass/fail • Identify significant variables:

• Students with no course access • Students that fail or do not submit

assessment • Course access


• Basic analytics to monitor engagement

• LMS (Blackboard) data • Failure of first assessment – trigger event

• Why a trigger at this point?


A replicable/ transferable model?: • ESAP – Enhancing Student Academic Potential

• Trained callers • Failure or non-submission of first assessment

• Focus on first year courses


A worked example: A replicable model?: • ESAP – Enhancing Student Academic Potential

• Trained callers • Failure or non-submission of first assessment

• Focus on first year courses Significant improvement in academic

performance No improvement in retention

High

Medium

Low

No risk

Course start to completion


Learning support

Intervention

High

Medium

Low

No risk



Establish earlier support processes

Intervention Touch points


Earlier interventions required • CBA – Classification based on Associations

• Association mining – all associations • CBA - Pre-determined target in this case student

retention


Risk indicators: Student demographic data Touch point 1 – pre-course commencement • Parental level of education • High School • Distance from University campus • International

High

Medium

Low

No risk




Touch points


What type of intervention does this information serve?


Advisory and preparatory conversation only What can teaching staff do with this information?


Risk indicators: Student demographic + LMS data • Course login • Comparative engagement • Parental level of education • High School • Distance from University campus • International


What type of intervention does this information serve? Poor SRL skills

High

Medium

Low

No risk






Risk indicators: Student demographic + LMS + assessment data • Assessment completion and score • Course login • Comparative engagement • Parental level of education • High School • Distance from University campus • International


What type of intervention does this information serve? Confirmatory – students do have poor SRL skills

High

Medium

Low

No risk





Summary Risk indicators at: pre-course commencement • Student demographic data; secondary education or

alternate entry

course commencement • Previous + Engagement with the university (e.g.

learnonline)

Mid course • Previous + formative and summative assessment


Impact: Retention initiative • 11160 students • 2158 (19.5%) classified as academically at risk • Attempts to contact all 2158 students • 1532 were contacted • 5.2% improvement on retention


Impact: Retention initiative • Trigger with greatest impact?

• Course access • Scalable, easy to interpret • Conversations have greater resonance


Dashboard

Dashboard

Dashboard

Dashboard

Dashboard

Dashboard

Quick example: Purdue University

Arnold, K. E., & Pistilli, M. D. (2012). Course Signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267-270), ACM.

Example

Example

Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414-2422.

Example

More than simple prediction

Is this accurate for all courses?

More than simple prediction

Example patterns

0

2

4

6

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12

1 2 3 4 5 6 7 8 9 10 11 12 13

Student login frequency avg / week

0

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1 2 3 4 5 6 7 8 9 10 11 12 13

Class average

0

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1 2 3 4 5 6 7 8 9 10 11 12 13

High performing student

0

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1 2 3 4 5 6 7 8 9 10 11 12 13

Typical login engagement pattern

0

10

20

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60

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90

100

-4 -2 0 2 4 6 8 10 12

Grades vs Time of first login (0 = course start)

0

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-4 -2 0 2 4 6 8 10 12

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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Females Internationalstudents

Otherlanguage at

home

Living in non-urban

Part timestudent

Previouslyenrolled to a

course

Early access Did notaccess

Late access

ACCT 1008 (n = 746)

BIOL 1007 (n = 220)

BIOL 1047 (n = 657)

COMM 1060 (n = 499)

COMP 1039 (n = 242)

ECON 1008 (n = 661)

GRAP 1017 (n = 192)

MARK 1010 (n = 723)

MATH 1063 (n = 194)

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

All coursestogether

ACCT1008 BIOL1007 BIOL1047 COMM1060 COMP1039 ECON1008 *GRAP1017

MARK1010 MATH1063

Model 1

Moodle

Model 1 + Moodle

Instructional conditions

Instructional conditions shape LA results

Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84.

Summary

1. Multiple drivers and new opportunities for LA/ EDM

Summary

1. Multiple drivers and new opportunities for LA/ EDM

2. LA is about generating insights into student learning for optimizing learning

Summary

1. Multiple drivers and new opportunities for LA/ EDM 2. LA is about generating insights into student learning for

optimizing learning

3. Multiple analytical approaches – each have their strengths and weaknesses

Summary


optimizing learning 3. Multiple analytical approaches – each have their

strengths and weaknesses

4. Correlations for the sake of correlation is NOT important

Summary



strengths and weaknesses 4. Correlations for the sake of correlation is NOT

important

5. What we do with information is critical. Timing and content

Summary



strengths and weaknesses 4. Correlations for the sake of correlation is NOT

important 5. What we do with information is critical. Timing and

content

6. Ultimately to improve learning we need to also improve teaching.

Questions

Questions?

Next session

1. Moving from course grained data to more fine grain 2. Social Network Analysis and introduction. 3. Video analytics 4. Why so few large scale examples of LA?

From course to fine

LMS/ SIS data - Course grained data Can we gain further insight?

Learning theory

“Traditional“ learning theory

Social learning theory

Where thinking happens

In the head Interactions with the world

Unit of analysis Individual Individual to community

Measurement focus

Outcomes Process

How learning happens

Transmission of information

Knowledge construction

I've become convinced that understanding how networks work is an essential 21st century literacy

Howard Rheingold

Social Network Analysis

One of the most common methods in LA What is SNA? Social network analysis [SNA] is the mapping and measuring of relationships between actors (people, groups, organisations, etc.). SNA can identify patterns of information flow and structure


History of SNA Graph theory, psychology and anthropology Seven bridges of Konigsberg


Seven bridges of Konigsberg – Graph Theory Euler path (Leonhard Euler 1707 – 1783)

Can you walk around the city crossing each bridge only once?


Jacob Moreno (1930’s) New York Training School for Girls Problem – large number of runaways in short period of time


Sociogram Graphic representation of the network structure • Actors (nodes) • Relations (lines) • Network (graph)

Basic concepts

• Tie strength – Identify strong/weak ties.

• Key players – Identify key/central nodes in the network

• Cohesion – A measure of the overall network structure


Granovetter, M. Strength of weak ties

Centrality

• Degree: number of connections an actor has (Kevin Bacon)

• Betweenness: high betweenness – links disparate clusters.

• Closeness: distance to all other actors in the network

• Density


Burt, R. (2005). Brokerage and closure: An introduction to social capital. Oxford, Oxford University Press.

Interpreting visualisations


Betweenness Broker (connects clusters)


Degree: Number of connections for a node (in degree and out degree) Degree = 7


Closure: Tight cluster, strong ties – trust building

Networks

Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014, March). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the Fourth International Conference, LAK (pp. 231-240). ACM.

Happy Network

Fowler, J. H., & Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. Bmj, 337, a2338.

Obesity Network

1 degree of separation 45% increase in probability a person is obese if social contact is obese 2 – 25%; 3 -10%; 4 - <5%

Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England journal of medicine, 357(4), 370-379.

“single most potent source of influence”

Astin, A. (1993). What matters in college: Four critical years revisited. San Francisco: Jossey-Bass.

Learning Network

Learning Networks

Gašević, D., Zouaq, A., & Janzen, R. (2013). “Choose Your Classmates, Your GPA Is at Stake!”: The Association of Cross-Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1460-1479. DOI: 10.1177/0002764213479362

Choose your classmates carefully – your GPA is at stake

• Social Networks Adapting Pedagogical Practice • Focus on student relationships (learning

networks) • Simple visualizations to assist with

interpretation and evaluate impact of activities • Lightweight analytics tool

• Bookmarklet • Rapid and easy dissemination

Visualisation

Visualisations

• Forum A • Forum B

14 messages posted by 4 participants

Visualisations

• Forum A • Forum B

Visualisations

Visualisations

Forum 1 Forum 2

Visualisations

Forum 1 Forum 2

Visualisations for interpretation

Monitoring online networks - Informed decisions for improving learning design - Evaluate impact of implemented activities

Network examples

Facilitator Centric Patterns • Interactions occurs between the facilitator and individual

participants but not between participants. • Indication that knowledge sharing and collaboration may

not be occurring e.g. Question and Answer Forums

Facilitator centric

Instructor

Disconnected students

Is this a learning community?

Facilitator centric

Instructor

Facilitator centric

Ego-networks • Top 10% • Bottom 10%

Ego-networks

Dawson, S. (2010). 'Seeing' the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736–752.

Top 10% student located in network

Student with a passing grade

Ego-networks

Low 10% student located in network

Students with a grade >75% < 90%

Ego-networks

• Staff intervention • High – 70% of networks • Low – 10% of networks • Why?

• The pursuit of community

Ego-networks

Course (unit)

Curriculum networks

Curriculum networks – dominant pathways

Curriculum networks

What outcomes, what experiences?

A F E D

C

B

Curriculum networks

What outcomes, what experiences? Assessment Learning outcomes Learning experiences Graduate attributes

Automated portfolio/ Learning Relationship

Curriculum networks

Can we predict student progression? What major? what courses? Analytical Hierarchy Process • Modelling based on student preferences and

motivation • Grades, BBA, SEoT, Professor, GPA, Assessment,

Timetable, Past course experience and demographics

Course prediction

Ognjanovic, I., Gasevic, D., & Dawson, S. (2016). Using institutional data to predict student course selections in higher education. The Internet and Higher Education, 29, 49-62.

• We can predict course and major choice • Timetable, Instructor

• Can we alter and influence course flow? • What learning experiences offer optimal value

at what time? • e.g. international experience; Work

integrated learning

Course Prediction

Ognjanovic, I., Gasevic, D., & Dawson, S. (2016). Using institutional data to predict student course selections in higher education. The Internet and Higher Education, 29, 49-62.

OVAL – Online Video Annotations for Learning

Video Analytics

Judgement of learning

Video Analytics

Student A (course 2 – graded)

Student B (course 4 – non-graded)

Video Analytics

Scalability and impact

Yet in terms of wide-scale institutional adoption there are few examples

Why?

LA/EDM research: • Predictions of learning success (early alerts) • Performance and retention • Indicators of on/off task attention • Carelessness and gaming • SRL proficiency • NLP • Learning dispositions • Graduate qualities/ 21st C literacies • Learning design

Research Summary

Great research BUT: • Ignores the complexity of university wide practices • Small scale and technology specific • Tends to be institutional specific • Lacks guidance to aid further adoption • Frequently requires high level skills and capacities

LA Research

Innovative research BUT: • Very few University wide examples of LA adoption • Obviously an area of increasing need and

importance

LA Research

National project to benchmark LA status, policy and practices for Australian Universities Many thanks to: Cassandra Colvin, Alex Wade and Tim Rogers

National Project

• understand current LA practice in Australia • unpack the challenges to institutional

adoption • Identify practices that can aid the

implementation of LA

Aims

2 complementary but separate studies • Study 1 – interviews with senior institutional leaders • Study 2 – concept mapping with LA expert panel

Approach

First study: Interviews with 32 Universities: • Identification of current practice, methods and

approaches • Identification of key drivers for institutions, stage

of development, process for implementation, project leads

Study 1

First study: Coding: • Development and application of coding protocol • Cluster analyses performed (PAM clusters)

Study 1

• Much interest in LA • Stated organisational priority

• LA projects were in the early phases of

implementation and small scale (at time of interview July 2014)

• 2 distinct clusters across variables such as: implementation, conceptualisation, readiness

Cluster 1 (n=15) – Solutions focused Cluster 2 (n=17) – Process focused

Study 1

High interest – slow uptake - predominantly at the stage of basic reporting • Goldstein & Katz (2005) reviewed US universities

and noted vast majority were in Stage 1 or 2 (of 5 stages in maturity)

• Yanosky (2009)

• Bichsel (2012)

Benchmarking

Clearly there remain challenges with implementing LA at scale

Benchmarking

2 Distinct trajectories for implementation • Differences in cluster variables such as:

• Conceptualisation of LA • Readiness • Implementation approach

Study 1

• Cluster 1 • focused on retention outcomes • Limited mention of LA as a means to

improve learning • Main driver is budget (cost savings)

• Cluster 2 • Broader view of learning analytics and its

application into learning and teaching practice

Conceptualisation

• Cluster 1 • Limited to no articulated strategy • Minimal capacity building activities • Success is seen as staff access to information • Technology infrastructure sound and

developing • Cluster 2

• Defined strategy • Developing capacity building activities • Technology – less emphasis on development.

Readiness

• Cluster 1 • Minimal stakeholder engagement • Leadership top down and siloed • Vendor tools heavily integrated

• Cluster 2

• Extensive stakeholder engagements • Leadership top down – but wide-spread. • Multiple engaged units (IT, teaching, faculty) • Where vendor tools/ processes adopted

maintained critical perspective

Implementation

Strategic capability

Context

Student demographics

Student retention and performance

Govn accreditation

University group / ATN/ Rural/ Go8


1. Solutions focused

• LA to address a pressing need • Time sensitive

2. Process focused • Networked and integrated model • Minimal time pressures • Innovation and experimentation


What are the ideal dimensions for long term sustainable uptake of LA? • Invite to Australian and international LA experts • 28 completed the entire concept mapping phases • 3 phases – brainstorming; sorting and ranking of

statements • Following the final ranking phase – a 7 cluster

solution emerged.

Study 2

Study 2

Bringing it together

Study 1 – 2 clusters Study 2 – 7 clusters Essentially – how an organisation approaches its conceptualisation of LA underpins (2 clusters) the method for deployment and adoption (7 clusters)

Systems Model


Interested Implementing

Implementation capability

Tool/Data Quality

Research/ Learning

Educator uptake

Systems Model


Implementing

Tool/Data Quality

Educator uptake

Systems Model


Interested Implementing

Implementation capability

Tool/Data Quality

Research/ Learning

Educator uptake

LA Maturity

LA Maturity

Solutions focused Address an immediate concern – e.g. retention

LA Maturity

Process focused Broad view of LA

LA Maturity

Merged model – responsive and agile

Challenges to be addressed: • Leadership awareness • Teams are seldom interdisciplinary • IT driven and system focused • Scale versus understanding • Capabilities and skills deficit. • Over reliance on current research – requires

further validation across different contexts to demonstrate transportability of models

Bringing it together

Leveraging the outcomes of short term goals for long term gain

• How do we merge both models to gain both

short and long term impact?

Complexity

Complexity

Law of requisite variety (Ashby 1958) • To control a system – the number of problems

need to be matched by at least an equal number of responses

• The more complex the system the more problems

Ashby W.R. (1958) Requisite variety and its implications for the control of complex systems, Cybernetica 1:2, p. 83-99. (available at http://pcp.vub.ac.be/Books/AshbyReqVar.pdf, republished on the web by F. Heylighen—Principia Cybernetica Project)

Complexity

Law of requisite complexity (Boisot & McKelvey 2011) • It take complexity to defeat complexity • A system must possess a level of complexity that

at least matches that of its environment to function effectively

Boisot, M., & McKelvey, B. (2011). Complexity and organization--Environment Relations: Revisiting Ashby's Law of Requisite Variety. The Sage handbook of complexity and management, 279-298.

Education is a complex system • Resilient to change • Adaptive and self-organising clusters • Change is non-linear and often unexpected

LA is complex – tendency towards simplification for implementation

Complexity

LA often reduced to independent components • Data • Analysis • Technology • Dashboards/ visualisations • Staff training

Does not adequately deal with the inter-relationships nor the overarching complexity of the system

Complexity

• The view to simplify can lead to fixed boundaries and organisational silos

• IT example

Complexity

Conclusion

• LA requires new models for implementation and

leadership • Enabling leadership • Whole of organisation • Models that are agile and research informed

• Working in complexity creates friction

• Embrace the friction – generates innovation

Conclusion

• A solutions based model can drive change – but

need to be mindful of responding to changing organsational needs

• Process based model can drive innovation and interest – but need to be mindful of how to scale

Conclusion

• Any action aimed at only a single element of the

system if student retention is unlikely to have impact

• Target multiple elements in the system – be adaptive and monitor change

Forsman, J., Van den Bogaard, M., Linder, C., & Fraser, D. (2015). Considering student retention as a complex system: a possible way forward for enhancing student retention. European Journal of Engineering Education,40(3), 235-255.

Conclusion

• Combined model framed in the organisational

context • Small, diffuse pockets of innovation to build

capacity and build interest • View to scale adoption – demonstration of

impact (technical, pedagogical) • Distributed enabling leadership (complexity

leadership)

Conclusion

• Any “successful” adoption of LA will be dependent

on an institution’s ability to rapidly recognise and respond to the organisational culture and the concerns of all stakeholders.

Thank you…

[email protected] Twitter: @shaned07

Documents

Learning analytics - The Open University of Hong Kong of LA.pdf · [email protected] ... • Turnitin scores . ... Course Signals at Purdue: Using learning analytics to increase