The Structure and Logic of the Learning Analytics

FieldGeorge Siemens, PhD

January 9, 2013Teachers College

Columbia UniversityNew York

- Publicly funded research university- 38,000 students- One of four research universities in Alberta- Only US accredited Canadian university (MSCHE)- Bachelor, masters, doctoral degrees- Fully online

My Work

1. Social network analysis and concept/knowledge development (NSERC/D2L, with Gasevic, Dawson, Haythornthwaite)

2. Knowledge mapping and competencies

3. Structure/Logic of LA: epistemology/assumptions/trends/deployment/impact

4. Developing post-baccalaureate (Masters, 2014, fingers crossed) on data analytics

Previous EdLab sessions

BrusilovskiBakerWoolfStamper (future)

Structure of Learning Analytics Field

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.

Scope of focus is important factor

EDM: specific variables and factors

LA: systemic and in-context factors

(but this distinction is not hard; overlap and blurring is occurring)

9

Domains of LA activity & impact

Learning & knowledge growthNetwork analysis – social and knowledgeContent analysisPersonalization and adaptationPrediction & InterventionSystemic impact

Inter-disciplinary emphasis

Bringing technical, pedagogical, and social domains into dialogue with each other.

Analytics around social interactions

Analytics around learning content

Analytics in different spaces (digital/F2F)

Analytics on interaction with the learning system (university/k-12)

Analytics on intervention and adaptation

Assessment of analytics

Siemens, Long, 2011. EDUCAUSE Review

What is happening globally in LA

Africa: end userAustralia: end user, gov’t, networkChina: end userEurope/UK: end user, gov’t, networkHong Kong: end user, gov’tIndia: end user, networkLatin America: end userUSA/Canada: end user, gov’t, network

http://lakconference.org

literature and research in three primary domains: networks and social media, learning analytics and datamining, and the future of learning and learning institutions.

Open Learning Analytics

Trends (?) in literature

Xavier Ochoa

Logic of LA field

Logic of analytics

Sensemaking and wayfinding

Comp683, Stat110 (Blitzstein)

What is research/science?

Essentially, discovery (identification) of connections

Validity of connection interrogation techniques

(Guba & Lincoln, 1994, 2005)

The research model in learning analytics– Holistic, not reductionist– Focused on systemic level– Impact and in-context evaluation– Social, technical, pedagogical

An EDM paper (best paper 2012 conf)

Terms: Student model, CTA, cognitive tutor, instructional techniques

Goal: automated technique for the discovery of better student models using input from previously generated models.

Data: DataShop, 300 datasets, 70 million student actions

Methodology: Knowledge components mapped to instructional tasks, LFA algorithm to find better models

A LAK paper (2012 conf)

Terms: learning sciences, engagement, design, learning ‘power’, competencies

Goals: “a learning analytics infrastructure for gathering data at scale, managing stakeholder permissions, the range of analytics that it supports from real time summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners.

Data: survey data, Learning Warehouse, >40,000. Not auto-tracked, instead: learner self-disclosing

Methodology: ELLI visualization and validation using Learning Warehouse (ELLI intends to “provide educators with a practical tool to enable rapid assessment and intervention of a complex quality, to stimulate change in learners”)

A LAK paper (2012 conf)

Terms: networks, community structure, SNA, distributed learning

Goals: Resolving: “multiple means of participation, each with their own Interactional and social affordances…Events in these media may be logged in different formats and databases, disassociating actions that for participants were part of a single unified activity.”

Methodology: “developed an abstract transcript representation called the Entity-Event-Contingency (EEC) graph that provides a unified analytic artifact [33]; and an analytic hierarchy derived from the EEC that supports multiple levels of analysis”

Data: Participant interaction in TappedIn, 150k members

Challenges

Scope of data captureIdentifying critical elements that matterGenerating multi-dimensional models

“Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked”

Suthers, Rosen, 2011

http://mashe.hawksey.info/2012/11/cfhe12-analysis-summary-of-twitter-activity/

Systemic: Impact and deployment

gsiemens @gmailTwitterSkypeFBWherever

www.elearnspace.org

www.connectivism.ca

www.learninganalytics.net