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Chapter 5: Predictive Modelling in Teaching and Learning

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Chapter 5: Predictive Modelling in Teaching and Learning Chapter 5: Predictive Modelling in Teaching and Learning Christopher Brooks 1, Craig Thompson 2 1 School of Information, University of Michigan, USA 2 Department of Computer Science, University of Saskatchewan, Canada DOI: 10.18608/hla17.005 ABSTRACT This article describes the process, practice, and challenges of using predictive modelling LQWHDFKLQJDQGOHDUQLQJ,QERWKWKHāHOGVRIHGXFDWLRQDOGDWDPLQLQJ ('0 DQGOHDUQLQJ analytics (LA) predictive modelling has become a core practice of researchers, largely with DIRFXVRQSUHGLFWLQJVWXGHQWVXFFHVVDVRSHUDWLRQDOL]HGE\DFDGHPLFDFKLHYHPHQW,QWKLV chapter, we provide a general overview of considerations when using predictive modelling, the steps that an educational data scientist must consider when engaging in the process, DQGDEULHIRYHUYLHZRIWKHPRVWSRSXODUWHFKQLTXHVLQWKHāHOG Keywords: 3UHGLFWLYHPRGHOLQJPDFKLQHOHDUQLQJHGXFDWLRQDOGDWDPLQLQJ ('0 IHDWXUH selection, model evaluation 3UHGLFWLYHDQDO\WLFVDUHDJURXSRIWHFKQLTXHVXVHG and journals associated with the Society for Learning to make inferences about uncertain future events. Analytics and Research (SoLAR) and the International In the educational domain, one may be interested in (GXFDWLRQDO'DWD0LQLQJ6RFLHW\ ,('06 IRUPRUH predicting a measurement of learning (e.g., student H[DPSOHVRIDSSOLHGHGXFDWLRQDOSUHGLFWLYHPRGHOOLQJ academic success or skill acquisition), teaching (e.g., First, it is important to distinguish predictive mod- WKHLPSDFWRIDJLYHQLQVWUXFWLRQDOVW\OHRUVSHFLāF HOOLQJIURPH[SODQDWRU\PRGHOOLQJ 7,QH[SODQDWRU\ LQVWUXFWRURQDQLQGLYLGXDO RURWKHUSUR[\PHWULFV modelling, the goal is to use all available evidence of value for administrations (e.g., predictions of re- WRSURYLGHDQH[SODQDWLRQIRUDJLYHQRXWFRPH)RU WHQWLRQRUFRXUVHUHJLVWUDWLRQ 3UHGLFWLYHDQDO\WLFV instance, observations of age, gender, and socioeco- in education is a well-established area of research, nomic status of a learner population might be used and several commercial products now incorporate LQDUHJUHVVLRQPRGHOWRH[SODLQKRZWKH\FRQWULEXWH predictive analytics in the learning content manage- to a given student achievement result. The intent of 1 2 PHQWV\VWHP HJ'/ 6WDUāVK5HWHQWLRQ6ROXWLRQV WKHVHH[SODQDWLRQVLVJHQHUDOO\WREHFDXVDO YHUVXV 3 4 Ellucian, and Blackboard )XUWKHUPRUHVSHFLDOL]HG correlative alone), though results presented using these 5 companies (e.g., Blue Canary, Civitas Learning ) now DSSURDFKHVRIWHQHVFKHZH[SHULPHQWDOVWXGLHVDQG provide predictive analytics consulting and products rely on theoretical interpretation to imply causation for higher education. (as described well by Shmueli, 2010). ,QWKLVFKDSWHUZHLQWURGXFHWKHWHUPVDQGZRUNĂRZ In predictive modelling, the purpose is to create a model related to predictive modelling, with a particular that will predict the values (or class if the prediction emphasis on how these techniques are being applied does not deal with numeric data) of new data based on in teaching and learning. While a full review of the REVHUYDWLRQV8QOLNHH[SODQDWRU\PRGHOOLQJSUHGLFWLYH literature is beyond the scope of this chapter, we en- modelling is based on the assumption that a set of known courage readers to consider the conference proceedings data (referred to as training instances in data mining 1 http://www.d2l.com/ 7 Shmueli (2010) notes a third form of modelling, descriptive 2KWWSZZZVWDUāVKVROXWLRQVFRP PRGHOOLQJZKLFKLVVLPLODUWRH[SODQDWRU\PRGHOOLQJEXWLQZKLFK 3 http://www.ellucian.com/ there are no claims of causation. In the higher education literature, 4 http://www.blackboard.com/ we would suggest that causation is often implied, and the majority 5 http://bluecanarydata.com/ of descriptive analyses are actually intended to be used as causal http://www.civitaslearning.com/ HYLGHQFHWRLQĂXHQFHGHFLVLRQPDNLQJ CHAPTER 5 PREDICTIVE MODELLING IN TEACHING & LEARNING PG 61 literature) can be used to predict the value or class of PREDICTIVE MODELLING new data based on observed variables (referred to as WORKFLOW features in predictive modelling literature). Thus the SULQFLSDOGLIIHUHQFHEHWZHHQH[SODQDWRU\PRGHOOLQJ Problem Identification and predictive modelling is with the application of the In the domain of teaching and learning, predictive PRGHOWRIXWXUHHYHQWVZKHUHH[SODQDWRU\PRGHOOLQJ modelling tends to sit within a larger action-oriented does not aim to make any claims about the future, HGXFDWLRQDOSROLF\DQGWHFKQRORJ\FRQWH[WZKHUHLQ- while predictive modelling does. stitutions use these models to react to student needs 0RUHFDVXDOO\H[SODQDWRU\PRGHOOLQJDQGSUHGLFWLYH in real-time. The intent of the predictive modelling modelling often have a number of pragmatic differ- activity is to set up a scenario that would accurately HQFHVZKHQDSSOLHGWRHGXFDWLRQDOGDWD([SODQDWRU\ describe the outcomes of a given student assuming PRGHOOLQJLVDSRVWKRFDQGUHĂHFWLYHDFWLYLW\DLPHG no new intervention. For instance, one might use a at generating an understanding of a phenomenon. predictive model to determine when a given individual 3UHGLFWLYHPRGHOOLQJLVDQLQVLWXDFWLYLW\LQWHQGHGWR is likely to complete their academic degree. Applying make systems responsive to changes in the underlying this model to individual students will provide insight data. It is possible to apply both forms of modelling into when they might complete their degrees assuming to technology in higher education. For instance, Lonn no intervention strategy is employed. Thus, while it is and Teasley (2014) describe a student-success system important for a predictive model to generate accurate EXLOWRQH[SODQDWRU\PRGHOVZKLOH%URRNV7KRPSVRQ scenarios, these models are not generally deployed and Teasley (2015) describe an approach based upon without an intervention or remediation strategy in mind. predictive modelling. While both methods intend Strong candidate problems for a successful predictive to inform the design of intervention systems, the modelling approach are those in which there are quan- former does so by building software based on theory WLāDEOHFKDUDFWHULVWLFVRIWKHVXEMHFWEHLQJPRGHOOHG GHYHORSHGGXULQJWKHUHYLHZRIH[SODQDWRU\PRGHOVE\ a clear outcome of interest, the ability to intervene in H[SHUWVZKLOHWKHODWWHUGRHVVRXVLQJGDWDFROOHFWHG situ, and a large set of data. Most importantly, there IURPKLVWRULFDOORJāOHV LQWKLVFDVHFOLFNVWUHDPGDWD must be a recurring need, such as a class being ordered The largest methodological difference between the two year after year, where the historical data on learners modelling approaches is in how they address the issue (the training set) is indicative of future learners (the RIJHQHUDOL]DELOLW\,QH[SODQDWRU\PRGHOOLQJDOORIWKH testing set). data collected from a sample (e.g., students enrolled in Conversely, several factors make predictive modelling a given course) is used to describe a population more PRUHGLIāFXOWRUOHVVDSSURSULDWH)RUH[DPSOHERWK generally (e.g., all students who could or might enroll in sparse and noisy data present challenges when trying DJLYHQFRXUVH 7KHLVVXHVUHODWHGWRJHQHUDOL]DELOLW\ WRFUHDWHDFFXUDWHSUHGLFWLYHPRGHOV'DWDVSDUVLW\RU are largely based on sampling techniques. Ensuring the missing data, can occur for a variety of reasons, such as sample represents the general population by reducing students choosing not to provide optional information. VHOHFWLRQELDVRIWHQWKURXJKUDQGRPRUVWUDWLāHGVDP - Noisy data occurs when a measurement fails to capture pling, and determining the amount of power needed the intended data accurately, such as determining a to ensure an appropriate sample, through an analysis VWXGHQWÚVORFDWLRQIURPWKHLU,3DGGUHVVZKHQVRPH RISRSXODWLRQVL]HDQGOHYHOVRIHUURUWKHLQYHVWLJDWRU VWXGHQWVDUHXVLQJYLUWXDOSULYDWHQHWZRUNV SUR[LHV is willing to accept. In a predictive model, a hold out used to circumvent region restrictions, a not uncommon dataset is used to evaluate the suitability of a model practice in countries such as China). Finally, in some IRUSUHGLFWLRQDQGWRSURWHFWDJDLQVWWKHRYHUāWWLQJ domains, inferences produced by predictive models of models to data being used for training. There are may be at odds with ethical or equitable practice, several different strategies for producing hold out such as using models of student at-risk predictions datasets, including k-fold cross validation, leave-one- WROLPLWWKHDGPLVVLRQVRIVDLGVWXGHQWV H[HPSOLāHG RXWFURVVYDOLGDWLRQUDQGRPL]HGVXEVDPSOLQJDQG LQ6WULSOLQJHWDO DSSOLFDWLRQVSHFLāFVWUDWHJLHV Data Collection With these comparisons made, the remainder of this In predictive modelling, historical data is used to gen- chapter will focus on how predictive modelling is erate models of relationships between features. One being used in the domain of teaching and learning, RIWKHāUVWDFWLYLWLHVIRUDUHVHDUFKHULVWRLGHQWLI\WKH and provide an overview of how researchers engage outcome variable (e.g., grade or achievement level) as in the predictive modelling process. well as the suspected correlates of this variable (e.g., JHQGHUHWKQLFLW\DFFHVVWRJLYHQUHVRXUFHV *LYHQ the situational nature of the modelling activity, it is PG 62 HANDBOOK OF LEARNING ANALYTICS important to choose only those correlates available at categorical, and interval and ratio are considered as or before the time in which an intervention might be numeric. Categorical values may be binary (such as HPSOR\HG)RULQVWDQFHDPLGWHUPH[DPLQDWLRQJUDGH predicting whether a student will pass or fail a course) PLJKWEHSUHGLFWLYHRIDāQDOJUDGHLQWKHFRXUVHEXW or multivalued (such as predicting which of a given set of if the intent is to intervene before the midterm, this possible practice questions would be most appropriate data value should be left out of the modelling activity. IRUDVWXGHQW
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