Policy Brief November 2012
UNESCO Institute for Information Technologies in Education
CONTENTS: CONTEXT LEARNING ANALYTICS EXAMPLES OF LEARNING ANALYTICS IMPACT AT DIFFERENT LEVELS LEARNING ANALYTICS DEBATES RECOMMENDATIONS REFERENCES
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?
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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
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.
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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
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.
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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
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
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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.
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
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REFERENCES (All links are illustra ve and should not be taken as endorsements of products) 
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FuturICT, The FuturICT Flagship Report (4 May 2012). FuturICT Flagship Consor um. h p://www. futurict.eu, 2012
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ELI, Seven Things You Should Know About First Genera on Learning Analy cs. EDUCAUSE Learning Ini a ve Brieﬁng, 2011. h p://www.educause.edu/library/resources/7-things-you-should-knowabout-ﬁrst-genera on-learning-analy cs
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IITE Policy Brief
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
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