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Policy Brief November 2012

UNESCO Institute for Information Technologies in Education



CONTEXT “Analy cs” is a term used in business and science to refer to computa onal support for capturing digital data to help inform decision-making. With the growth of huge data sets and computa onal power, this extends to designing infrastructures that exploit rapid feedback, to inform more mely interven ons, whose impact can in turn be monitored. Organisa ons have increasingly sensi ve ‘digital nervous system’ providing real me feedback on the external environment and the e ects of ac ons. Learning Analy cs appropriates this concept for educa on: what should a digital nervous system look like when the focus is on learning outcomes, and to extend the metaphor, what kind of ‘brain’ or collec ve intelligence is needed to interpret the signals and adapt the system’s behavior accordingly?

IITE Policy Brief

November 2012

Big Data As people and devices are increasingly connected online, society is genera ng digital data traces at an extraordinary rate, unprecedented in human history.1, 2 Social compu ng, networked appliances, e-business transac ons, mobile compu ng, wearable ‘lifelogging’ sensors, and environmental scanners generate billions of events per second, many of which are stored for later analysis, or can be analysed as a realme data stream. The term “Big Data” is used to reflect that a quan ta ve shi of this magnitude is in fact a qualita ve shi demanding new ways of thinking, and new kinds of human and technical infrastructure. This raises a host of opportuni es and challenges for society at large, and for ins tu ons seeking to make sense of this data. Cri cal debates are developing around what is required to ensure that society can convert this “new oil” into a public good by fostering new kinds of literacies and ethics, and combining commercial services with open data and services.3, 4

Business Intelligence Within commercial sectors, the field of Business Intelligence (BI) is establishing itself, seeking to equip ins tu ons so that they can iden fy meaningful pa erns in the data, using an array of technologies including data integra on, data mining, predic ve modelling and informa on visualiza on.5 However, technology alone is just part of the story: appropriately skilled analysts are needed to make sense of the data, in order to inform decision-making, but the pace of development is outstripping the supply of such people.2 Given the talent gap, and the culture shi needed to share and integrate data across organisa onal silos, proper embedding of such infrastructure requires senior leadership coupled with communica on and training, championed by ‘BI competency centres’. All ins tu ons face the economic and business pressures to do more with less, and be publicly accountable, and are understandably a racted to strategies claiming to enhance collec ve capacity to orchestrate data, use this to inform decisions, and evidence impacts.

LEARNING ANALYTICS Learning analy cs has emerged as one of the most common terms for the community seeking to understand the implica ons of these developments for how we analyse learning data, and improve learning systems through evidence-based adapta on. The emerging conversa on goes far beyond technologists (academic and commercial), to include researchers in educa on, leaders and policymakers, educa onal prac oners, organisa onal administrators, instruc onal designers, product vendors, and cri cally, the learners themselves (who are o en the first adopters of new cloud applica ons, many of which make data available, and who are the primary consumers of certain kinds of learning analy c). There are many good introduc ons to Learning Analy cs, which add valuable perspec ves.6-10


Learning Analytics

The Convergence of Macro, Meso and Micro-level Analytics Learning analy cs cover a wide range of analy c, which we will define as macro-, mesoand micro-levels: • Macro-level analy cs seek to enable crossins tu onal analy cs, for instance, through ‘maturity’ surveys of current ins tu onal prac ces11 or improving state-wide data access to standardized assessment data over students’ life mes.12 Macro-analy cs will become increasingly real- me, incorpora ng more data from the finer-granularity meso/micro levels, and could conceivably benefit from benchmarking and data integra on methodologies developed in non-educa onal sectors (although see below for concerns about the dangers of decontextualized data and the educa onal paradigms they implicitly perpetuate).

Meso-level analy cs operate at ins tu onal level. To the extent that educa onal ins tu ons share common business processes to sectors already benefi ng from BI, they can be seen as a new BI market sector, who can usefully appropriate tools to integrate data silos in enterprise warehouses, op mize workflows, generate dashboards, mine unstructured data, be er predict ‘customer churn’ and future markets, and so forth. It is the BI impera ve to op mise business processes that partly mo vates e orts to build ins tu onallevel “academic analy cs”13, and we see communi es of prac ce specifically for BI within educa onal organisa ons, which have their own cultures and legacy technologies.14

Micro-level analy cs support the tracking and interpreta on of process-level data for individual learners (and by extension, groups). This data is of primary interest to learners themselves, and those responsible for their success, since it can provide the finest level of detail, ideally as rapidly as possible. This data is correspondingly the most personal, since (depending on pla orms) it can disclose online ac vity click-by-click, physical ac vity such as geoloca on, library loans, purchases, and interpersonal data such as social networks. Researchers are adap ng techniques from fields including serious gaming, automated marking, educa onal data mining, computer-supported collabora ve learning, recommender systems, intelligent tutoring systems/adap ve hypermedia, informa on visualiza on, computa onal linguis cs and argumenta on, and social network analysis.


IITE Policy Brief

November 2012

As the figure shows, what we now see taking place is the integra on of, and mutual enrichment between, these layers. Company mergers and partnerships show business intelligence products and enterprise analy cs capacity from the corporate world being integrated with course delivery and social learning pla orms that track micro-level user ac vity. The aggrega on of thousands of learners’ interac on histories across cohorts, temporal periods, ins tu ons, regions and countries creates meso + macro level analy cs with an unprecedented level of fine-grained process data (Scenario: comparing similar courses across ins tu ons for the quality of online discourse in final year poli cs students). In turn, the crea on of such large datasets begins to make possible the iden fica on and valida on of pa erns that may be robust across the idiosyncrasies of specific contexts. In other words, the breadth and depth at the macro + meso levels add power to micro-analy cs (Scenario: be er predic ve models and feedback to learners, because sta s cally, one may have greater confidence in the predic ve power of key learner behaviours when they have been validated against a na onally aggregated dataset, than from an isolated ins tu on).

EXAMPLES OF LEARNING ANALYTICS As the preceding list demonstrates, this briefing cannot possibly represent the field evenly. The following examples hint at the breadth of learning analy cs, with indica ons of their maturity. The Interna onal Conference on Learning Analy cs & Knowledge (LAK) has archived proceedings and replayable presenta ons which are the best snapshot of the emerging state of the art15 while EDUCAUSE is building a valuable resource bank and training events for educators/leaders/IT-administrators closer to immediate deployment op ons.16 In contrast to BI companies who are trying to understand how their products map to the educa on market ini ally at the meso/macro-levels, educa onal startup companies are accelera ng the pace at which learners will encounter micro-level analy cs (e.g. the Educa onal Innova on Summit17). An EDUCAUSE synthesis of emerging trends in 20129 iden fies three kinds of predictors and indicators (Disposi onal, Ac vity & Performance, and Student Ar facts), the key role of Visualiza on to support educa onal sensemaking (e.g. debate over what the analy cs appear to be evidencing), and two kinds of interven ons (fully and semi-automated).

LMS/VLE Analytics Dashboards Concept. The first kinds of analy cs that many ins tu ons will encounter will be the analy cs dashboards now appearing in most online learning pla orms. This is essen ally the impact of BI products on learning pla orms. Un l recently, data logs were not in a format that non-technical users could interpret, but these are now rendered via a range of graphs, tables and other visualiza ons, and custom reports designed for consump on by learners, educators, administrators and data analysts. More advanced func onality integrates data from other university systems (e.g. Helpdesk calls; Student Informa on Systems), and more powerful (but harder to learn) tools enable users to go beyond predefined reports and explore rela onships between di erent variables. Learners may get basic analy cs such as how they are doing rela ve to the cohort average (e.g. test


Learning Analytics

scores, forum contribu ons, webinar par cipa on). Some ins tu ons are going further, and add addi onal informa on visualiza on products to assist in making sense of complex data, or enterprise-level analy cs architectures from major vendors. Examples. LMS/VLE vendors provide examples and webinars about their analy cs dashboards, and the enterprise analy cs/BI vendors are contextualizing their products to the educa on market.18-24 A very useful compendium of higher educa on case studies is being compiled by EDUCAUSE, e.g. Arizona State University reports that it is seeing returns on its investment in academic and learning analy cs, including a “Student 360” programme that integrates all that the ins tu on knows about a student.25

Predictive Analytics Concept. One of the more advanced uses of analy cs that generates huge interest is the possibility that from the pa ern of learners’ sta c data (e.g. demographics; past a ainment) and dynamic data (e.g. pa ern of online logins; quan ty of discussion posts) one can classify the trajectory that they are on (e.g. “at risk”; “high achiever”; “social learner”), and hence make more mely interven ons (e.g. o er extra social and academic support; present more challenging tasks). Currently, one of the most reliable predictors of final exam results is s ll exam performance at the start of studies.26, 27 The design of more complex data-driven predic ve models must clearly improve on this, but requires sta s cal analysis to iden fy those variables in the data that can be historically validated as being the strongest predictors of ‘success’. While at present these are most commonly defined as assignment/exam outcomes, the debate about assessment regimes (see below) draws a en on to the role that analy cs could play in providing forma ve feedback and the building of horizontal/transferable skills. Examples. Work at Purdue University28 on the Course Signals so ware is well known, and the technology is available as a product.18 Signals provides a red/amber/green light to students on their progress. Their most recent evalua on reports: “Results thus far show that students who have engaged with Course Signals have higher average grades and seek out help resources at a higher rate than other students.” University of Michigan report promising results with physics students from their E2Coach infrastructure26 which adapts personalised (open source) interven on technology29 from validated health informa cs research, to give customised feedback and mo vate students to change their strategies. Paul Smith’s college used Starfish EarlyAlert30 to integrate sta feedback on students, and Rapid Insight tools24 to build an accurate predic ve model for iden fying at-risk students.31 Models may be context-specific to the par cular ins tu on, culture, level of study, discipline, etc., or (most exci ngly) may prove robust enough to generalise. The Predic ve Analy cs Repor ng (PAR) Framework, developed and piloted with six US educa onal ins tu ons, seeks to iden fy pa erns in their collec ve student data. Ini al results report a significant correla on between disenrollment and the number of concurrent courses in which students were enrolled.32 These approaches are designed for generic learning environments, agnos c to subject ma er, but if one constrains the scope to a specific topic, new kinds of analy cs are possible.


IITE Policy Brief

November 2012

Adaptive Learning Analytics Concept. Adap ve learning pla orms build a model of a learner’s understanding of a specific topic (e.g. algebra; photosynthesis; dental surgical procedures), some mes in the context of standardised tests which dictate the curriculum and modes of tes ng. This enables fine-grained feedback (e.g. which concepts you have grasped and at what level), and adap ve presenta on of content (e.g. not showing material that depends on having mastered concepts the learner has failed on). Naturally, dynamic modelling of learner cogni on, and prepara on of material for adap ve content engines, are far more resource intensive to design and build than conven onal ‘dumb’ learning pla orms. However, there is robust research evidence of the impact that such approaches can have, given the personaliza on that is possible.33 Examples. Significant research and investment in intelligent tutoring systems and adap ve hypermedia are bringing web pla orms to market with a high quality user experience, and this is likely to con nue to be a growth area. Examples include the free Open Learning Ini a ve34 courses based on Carnegie Mellon University’s research, and commercial services such as Grockit and Knewton.35, 36

Social Network Analytics Concept. Social network analysis (some mes called Organisa onal Network Analysis in corporate se ngs) makes visible the structures and dynamics of interpersonal networks, to understand how people develop and maintain these rela ons. People may form ‘ es’ of di erent sorts, ranging from extended, direct interac on reflec ng significant es, to more indirect es. Research is beginning to demonstrate that the connec ons learners forge with each other, and the resul ng group structures, can correlate with more or less e ec ve learning.37, 38 Examples. “Enterprise 2.0” products can be used to iden fy the most ac ve users in an online network, and those who are likely to have most influence on the ac vity of others.39 There are numerous free tools for interac ve visualisa on and analysis of networks.40 One tool specifically designed for learning networks is SNAPP41 which renders discussion forum pos ngs as a network diagram to help trace the growth of a cohort, iden fy disconnected students, or visualise how teacher support is employed within the network. Another is NAT, designed to help teachers see their o ine social networks, which annotates social es with the relevant topics.42

Discourse Analytics Concept. It is simple for a learning pla orm to count how many mes a learner has executed basic ac ons such as logging in, viewing a forum and pos ng a message: this is the level at which most current analy cs products operate. However, analy cs could go beyond simple quan ta ve logs, and provide feedback to educators and learners on the quality of the contribu ons. Researchers are beginning to draw on extensive prior work on how tutors mark essays and discussion posts, how spoken and wri en dialogue shape learning, and how computers can recognize good argumenta on, in order to design analy cs that can assess the quality of text, with the ul mate goal of sca olding the higher order thinking and wri ng that we seek to ins ll in students. Examples. Discourse analy cs specifically tuned for learning31, 32 or sensemaking in contested domains43, 44 are at the stage of research prototypes. There are numerous open


Learning Analytics

source research pla orms45 and enterprise grade products46 capable of analysing wri en and spoken natural language to assist computa onal reasoning, but they have not been designed with learning specifically in mind. As such, they represent raw technologies with intriguing possibili es for learning analy cs researchers to contextualize to educa on.

IMPACT AT DIFFERENT LEVELS Given the mul ple levels at which Learning Analy cs operate, and the rich diversity of numerical, textual and seman c data that di erent techniques process, their impact on higher educa on could be profound when implemented systemically, with sound pedagogical design and the necessary sta development to turn raw technologies into useful tools. In one briefing summarised below, several forms of impact are proposed, which are grouped using this briefing’s three-level scheme:8 Micro-level benefits: • Iden fy at-risk learners and provide interven ons.

Provide learners with insight into their own learning habits and give recommenda ons for improvement.

Meso-level benefits: • Improve administra ve decision-making and organiza onal resource alloca on.

More transparent data and analysis could create a shared understanding of the ins tu on’s successes and challenges.

Make be er sense of complex topics through combina ons of analy cs (e.g. from social, technical and informa on networks).

Support holis c decision-making through be er understanding the impact of di erent variables.

Increase organiza onal produc vity by providing up-to-date informa on and allowing rapid response to challenges.

Help leaders determine the hard (e.g. patents, research) and so (e.g. reputa on, profile, quality of teaching) value generated by faculty ac vity.

Macro-level benefits: • Ul mately the above might transform the college/university system, as well as academic models and pedagogical approaches.

Learning: Towards a Data-Driven Science? This last point merits further elabora on. Other scien fic fields are being transformed by big data and automated analy cs, introducing data-driven exploratory methodologies, and redefining the researcher’s workbench and skillset. The study of learning and teaching may be standing on the threshold of a similar revolu on, where for the first me learners can be studied at a scale and fidelity of ac on which was previously imprac cal. The free hos ng of learning pla orms and courses by ini a ves such as Harvard+MIT’s edX are quite openly mo vated by the opportuni es that come with the ownership of unprecedented data sets from millions of learners’ interac ons.47


IITE Policy Brief

November 2012

LEARNING ANALYTICS DEBATES Important debates are beginning to develop around the (o en implicit) assump ons underpinning learning analy cs, and by extension, their limita ons if used crudely.

Data Is Not Neutral Informa on systems filter and categorise the world. When done well, simplified models help us grasp overwhelming complexity, but done badly, they ignore important details. A marker of the health of the learning analy cs field will be the quality of debate around what the technology renders visible and leaves invisible. A recent cri que of the rhetoric around Big Data reminds us to enter this field with cau on:48

• • • • • •

Automa ng Research Changes the Defini on of Knowledge Claims to Objec vity and Accuracy are Misleading Bigger Data are Not Always Be er Data Not All Data Are Equivalent Just Because it is Accessible Doesn’t Make it Ethical Limited Access to Big Data Creates New Digital Divides

In the context of learning analy cs, every step of the lifecycle — from data to analy cs to insight to interven on — is infused with human judgment. In short, it is as naïve to believe that ‘data speaks for itself’ as it is to believe that a text has a single, objec vely discernible meaning for all contexts.

Learning Analytics Perpetuate Assessment Regimes Learning analy cs are intended to improve student success. They are, consequently, always designed with a par cular concep on of ‘success’, thus defining the pa erns deemed to be evidence of progress, and hence, the data that should be captured. The primary driver of mainstream teaching prac ce, and hence the learner’s experience, is the assessment regime. Micro-level learning analy cs are in essence, new assessment technology, capable at their best of providing personalized, mely, specific, ac onable feedback. Since assessment regimes are a hotly contested issue within educa onal research and policy, by extension, an intelligent approach to learning analy cs must engage with this debate, making clear what assessment regimes and pedagogical commitments a given learning analy c promotes. Due to the complexity of implemen ng good assessment for learning,49 designing tools of this sort remains a primary challenge for learning analy cs researchers.50, 51 The promise is that done well, analy cs could be the key enabler for delivering forma ve assessment for learning at scale, placing new kinds of tools in the hands of learners.52 The risk is that research and development focuses on the data which is simplest to log computa onally, perpetua ng the dominant pedagogies and learning outcomes from an industrial era, when most educa onal thought-leaders point to the addi onal disposi ons and skills needed for lifelong, lifewide learning, and the capacity to thrive in a very turbulent world.


Learning Analytics

Ethics As in any field concerned with the sharing and interpreta on of personal data, ethical issues pervade learning analy cs. Who decides which data are important to log, how it is ‘cleaned’ for aggrega on with other datasets, and whether those datasets are compa ble? Who decide how the data are rendered visually, and are those seeing them literate enough to interpret them? Should learners see analy cs about themselves, or their peers? Are teachers skilled enough to devise appropriate interven ons based on them? Can data be anonymised adequately, and can access be controlled appropriately? Are a empts to formalise educa onal theories to embed them in computa onal algorithms valid? The research field requires informa on technology ethicists to inform its work, since most if not all the issues around learning analy cs have arisen in other domains.

RECOMMENDATIONS In the light of the preceding analysis, the following recommenda ons are proposed to help educa onal and business leaders orient to the opportuni es and risks, and to catalyse a wider debate around the educa onal worldview underpinning Learning Analy cs. 1. Learning Analy cs are never neutral: they unavoidably embody and thus perpetuate par cular pedagogy and assessment regimes. Changing these is a profound challenge that spans the micro—macro, given the iner a to thinking about assessment in fresh ways in the educa onal ecosystem (primary/secondary/ter ary/workplace). Governments and ins tu ons can use the possible introduc on of analy cs to catalyse debate on their vision for teaching and learning for the 21st Century. 2. There is a pressing need to plug the widening analy cs talent gap. Ins tu ons should train sta and researchers in the design and evalua on of learning analytics — to ensure that there is the organisa onal capacity to deploy analy cs with integrity, sustain quality dialogue about how they are used, ask the right ques ons of vendors, and to sa sfy the societal demand for this workforce. 3. Compared to many other sectors, educa onal ins tu ons are currently ‘driving blind’. They should invest in analy cs infrastructures for two reasons: (1) to op mise student success, and (2) to enable their own researchers to ask founda onal ques ons about learning and teaching in the 21st century. To research learning without an analy cs infrastructure may soon become like a theore cal physicist with no access to CERN, or a gene cist without genome databases. 4. The field is moving fast, with companies innova ng to meet perceived markets. To keep up, the normally slower pace of educa onal research and professional development must be accelerated, or ins tu ons are at risk of making purchasing decisions based on what’s available, rather than what’s needed. Ins tu ons should collaborate on establishing trusted partnerships and robust mechanisms to share student data, analy cs techniques and informa on visualiza on tools. To complement innova on driven by what can be done economically on today’s infrastructure and taken to market rapidly, we need to turbocharge innova on which is driven by research-validated, educa onally sound prac ce and next genera on technologies. An open analy cs pla orm and community is one proposed vehicle for such work.53


IITE Policy Brief

November 2012

REFERENCES (All links are illustra ve and should not be taken as endorsements of products) [1]

Bollier, D., The Promise and Peril of Big Data. The Aspen Ins tute, CO, USA, 2010. h p://www. aspenins ons/promise-peril-big-data


Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A. H., Big data: The next fron er for innova on, compe on, and produc vity. McKinsey Global Ins tute, 2011. h p:// on/big_data_the_next_ fron er_for_innova on


Anderson, J. A. and Rainie, L., The Future of Big Data Pew Research Center’s Internet & American Life Project, 2012. h p://


FuturICT, The FuturICT Flagship Report (4 May 2012). FuturICT Flagship Consor um. h p://www., 2012


Chaudhuri, S., Dayal, U. and Narasayya, V., An Overview of Business Intelligence Technology. Communica ons of the ACM, 54, 8, (2011), 88-98. h p://


JISC-CETIS, Analy cs; What is Changing and Why Does it Ma er? UK Joint Informa on Systems Commi ee: Centre for Educa onal Technology and Interoperability Standards: CETIS Analy cs Series, 1 (1), 2012. h p://publica ons.ce cs-Vol1No1-Briefing-Paper-online.pdf


ELI, Seven Things You Should Know About First Genera on Learning Analy cs. EDUCAUSE Learning Ini a ve Briefing, 2011. h p:// on-learning-analy cs


Long, P. and Siemens, G., Penetra ng the fog: analy cs in learning and educa on. Educause Review Online, 46, 5, (2011), 31-40. h p:// cle/penetra ng-fog-analy cslearning-and-educa on


Brown, M., Learning Analy cs: Moving from Concept to Prac ce. EDUCAUSE Learning Ini a ve Briefing, 2012. h p:// cs-moving-concept-prac ce

[10] Siemens, G., Learning analy cs: envisioning a research discipline and a domain of prac ce. In: Proceedings of the 2nd Interna onal Conference on Learning Analy cs and Knowledge (Vancouver, Bri sh Columbia, Canada, 2012). ACM. pp.4-8 [11] Norris, D., Baer, L., Leonard, J., Pugliese, L. and Lefrere, P., Ac on Analy cs: Measuring and Improving Performance That Ma ers in Higher Educa on. EDUCAUSE Review, 43, 1, (2008), 42-67. h p:// cle/ac on-analy cs-measuring-and-improving-performancema ers-higher-educa on [12] DQC: Data Quality Campaign. h p:// [13] Campbell, J. P. and Oblinger, D. G., Academic Analy cs. Educause, 2007. h p:// library/pdf/PUB6101.pdf [14] UBIC: Universi es Business Intelligence Collabora on: LinkedIn Group. h p:// groups?home=&gid=3676293 [15] LAK: Interna onal Conference on Learning Analy cs & Knowledge. h p:// events/lak [16] EDUCAUSE: Analy cs in Higher Educa on. h p:// cs [17] EIS-ASU: Educa on Innova on Summit. h p://edinnova [18] Ellucian: Course Signals. h p:// [19] SAS: SAS for Higher Educa on. h p:// on/highered [20] IBM: Analy cs for Educa on. h p:// ware/analy cs/educa on [21] Blackboard: Analy cs. h p:// orms/analy cs/overview.aspx [22] Canvas: Analy cs. h p:// cs [23] Desire2Learn: Analy cs. h p:// cs [24] Insight, R.: Veera & Analy cs Products. h p://www.rapidinsigh on [25] Crow, M. M., No More Excuses. EDUCAUSE Review Online, July/Aug., (2012). h p://www.educause. edu/ero/ar cle/no-more-excuses-michael-m-crow-analy cs


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[26] McKay, T., Miller, K. and Tritz, J., What to do with ac onable intelligence: E<sup>2</sup>Coach as an interven on engine. In: Proceedings of the 2nd Interna onal Conference on Learning Analy cs and Knowledge (Vancouver, Bri sh Columbia, Canada, 2012). ACM. pp.88-91 [27] Buckingham Shum, S.: Learning Analy cs: Notes on the Future (slide 13). h p://www.slideshare. net/sbs/learning-analy cs-notes-on-the-future [28] Pis lli, M. D., Arnold, K. and Bethune, M., Signals: Using Academic Analy cs to Promote Student Success. EDUCAUSE Review Online, July/Aug., (2012). h p:// cle/signalsusing-academic-analy cs-promote-student-success [29] MTS: The Michigan Tailoring System. h p:// [30] Starfish: EarlyAlert. h p://www.starfishsolu ons/earlyalert.html [31] Taylor, L. and McAleese, V., Beyond Reten on: Using Targeted Analy cs to Improve Student Success. Educause Review Online, July/Aug., (2012). h p:// cle/beyondreten on-using-targeted-analy cs-improve-student-success [32] Wagner, E. and Ice, P., Data Changes Everything: Delivering on the Promise of Learning Analy cs in Higher Educa on. EDUCAUSE Review Online, July/Aug., (2012). h p:// ar cle/data-changes-everything-delivering-promise-learning-analy cs-higher-educa on [33] Love , M., Meyer, O. and Thille, C., The Open Learning Ini a ve: Measuring the e ec veness of the OLI sta s cs course in accelera ng student learning. Journal of Interac ve Media in Educa on, 14, (2008). h p:// [34] OLI: Open Learning Ini a ve, Carnegie Mellon University. h p:// [35] Grockit: Online Test Prepara on. h ps:// [36] Knewton: Adap ve Learning Pla orm. h p:// [37] Dawson, S., ‘Seeing’ the learning community: An explora on of the development of a resource for monitoring online student networking. Bri sh Journal of Educa onal Technology, 41, 5, (2010), 736-752 [38] Haythornthwaite, C. and De Laat, M., Social networks and learning networks: using social network perspec ves to understand social learning. 2010 [39] Mzinga: OmniSocial. h p:// ware/omnisocial.asp [40] Gephi: Open Graph Visualiza on Pla orm. h p:// [41] SNAPP: Social Networks Adap ng Pedagogical Prac ce. h p:// learningnetworks/seeing/snapp [42] Bieke, S. and Maarten, D. L., Network awareness tool - learning analy cs in the workplace: detec ng and analyzing informal workplace learning. In: Proceedings of the 2nd Interna onal Conference on Learning Analy cs and Knowledge (Vancouver, Bri sh Columbia, Canada, 2012). ACM. pp.59-64 [43] Argunaut: Research Publica ons. h p:// ons/publica ons/argunautpublica ons/ [44] De Liddo, A., Sándor, Á. and Buckingham Shum, S., Contested Collec ve Intelligence: Ra onale, Technologies, and a Human-Machine Annota on Study. Computer Supported Coopera ve Work, 21, 4-5, (2012), 417-448. h p:// [45] GATE: General Architecture for Text Engineering. h p:// [46] Autonomy: Autonomy: Natural Language Processing. h p:// Func onality/idol-func onality-nlp [47] edX: edX. h ps://h p:// [48] Boyd, d. and Crawford, K., Six Provoca ons for Big Data. In: A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society (Oxford Internet Ins tute: Oxford, UK, 21 Sept. 2011, 2011). h p:// [49] ARG, Assessment for Learning: 10 Principles. Assessment Reform Group (assessment-reform-group. org). 2002. h p:// [50] Booth, M., Learning Analy cs: The New Black. EDUCAUSE Review Online, July/Aug., (2012). h p:// cle/learning-analy cs-new-black [51] Campbell, G., Blog post: “Analy cs” interven ons (11 July). 2011. h p:// blog1/?p=1499 [52] Buckingham Shum, S. and Ferguson, R., Social Learning Analy cs. Educa onal Technology & Society (Special Issue on Learning & Knowledge Analy cs, Eds. G. Siemens & D. Gaševi ), 15, 3, (2012), 3-26. h p:// Open Access Eprint: h p:// [53] SoLAR, Open Learning Analy cs. White Paper: Society for Learning Analy cs Research, 2012. h p:// cs.pdf


IITE Policy Brief

November 2012

Learning Analy cs is a rapidly growing research field and commercial market, with poten ally disrup ve poten al. While educa onal researchers have for many years used computa onal techniques to analyse learner data, generate visualiza ons of learning dynamics, and build predic ve models to test theories — for the first me, these techniques are becoming available to educators, learners and policy makers. Learning analy cs promise is to transform educa onal research into a data-driven science, and educa onal ins tu ons into organisa ons that make evidence-based decisions. However, cri cal debate is needed on the limits of computa onal modelling, the ethics of analy cs, and the educa onal paradigms that learning analy cs promote.

Author: Simon Buckingham Shum

Published by the UNESCO Ins tute for Informa on Technologies in Educa on 8 Kedrova St., Bldg. 3 Moscow, 117292 Russian Federa on Tel: +7 (499) 129 29 90 E-mail: h p://www.

© UNESCO, 2012 Printed in the Russian Federa on

ISSN 2221-8378

Fax: +7 (499) 129 12 25

UNESCO Policy Brief November 2012  

Learning Analytics

UNESCO Policy Brief November 2012  

Learning Analytics