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Data Analytics process in Learning and Academic
Analytics projects
Day 3: Data processing
Alex Rayón [email protected]
DeustoTech Learning – Deusto Institute of Technology – University of DeustoAvda. Universidades 24, 48007 Bilbao, Spain
www.deusto.es
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Table of contents
● Data dimensions● Applications● Data processing in an ETL refined data● Knowledge discovery
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Table of contents
● Data dimensions● Applications● Data processing in an ETL refined data● Knowledge discovery
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Data dimensionsSummary
[Verbert2011]
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Data dimensions1) Computing
● Software○ Example
■ Q1. Among the tools, which is more representative of the final grade?
■ Q5. Which is the impact of the social networks in the group composition?
■ Q6. Which tools are more prone to foster collaboration?
■ Q7. The use of some collaboration tools has effect on the final grade?
● Hardware● Network
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Data dimensions2) Location
● Quantitative● Qualitative
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Data dimensions3) Time
● Timestamp● Time interval
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Data dimensions4) Activity
● Events● Tasks● Goals● Subject
○ Example
■ Q2. Which are the differences in terms of grades
between this subject and other subjects where we already know the final grade?
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Data dimensions5) Physical condition
● Noise level● Lighting● ...
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Data dimensions6) Resource
● Physical resource● Virtual resource
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Data dimensions7) User
● Basic info○ Example
■ Q3. Is there any gender difference in the use of the tools?
● Knowledge● Interest● Goals
○ Short-term○ Long-term
● Learning styles● Affects● Background
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Data dimensions8) Relations
● Social relations○ Example
■ Q4. Are there groups of people that repeatedly collaborate in different tools?
■ Q4. Do these groups repeat over time?
● Functional relations● Compositional relations● Proximity● Orientation● Communication
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Table of contents
● Data dimensions● Applications● Data processing in an ETL refined data● Knowledge discovery
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ApplicationsWhy do learners use analytics?
[Ferguson2014]
● Monitor their own activities and interactions● Monitor the learning process● Compare their activity with that of others● Increase awareness, reflect and self reflect● Improve discussion participation● Improve learning behaviour● Improve performance● Become better learners● Learn!
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ApplicationsWhy do teachers use analytics?
[Ferguson2014]
● Monitor the learning process● Explore student data● Identify problems● Discover patterns● Find early indicators for success● Find early indicators for poor marks or drop-
out● Assess usefulness of learning materials
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ApplicationsWhy do teachers use analytics? (Ii)
● Increase awareness, reflect and self reflect● Increase understanding of learning
environments● Intervene, advise and assist● Improve teaching, resources and the
environment
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Table of contents
● Data dimensions● Applications● Data processing in an ETL refined data● Knowledge discovery
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Data processingTransform menu
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Data processingScripting menu
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Data processingJoins menu
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Data processingStatistics menu
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Data processingWEKA plugin
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Data processingWEKA plugin (II)
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Data processingWEKA plugin (III)
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Data processingWEKA plugin (IV)
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Data processingWEKA plugin (V)
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Table of contents
● Data dimensions● Applications● Data processing in an ETL refined data● Knowledge discovery
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Knowledge discoveryIntroduction
[BakerSiemens2014]
This review draws on past reviews (cf. Baker & Yacef, 2009; Romero & Ventura, 2010; Ferguson, 2012; Siemens & Baker, 2012)
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Knowledge discoveryIntroduction (II)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
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Knowledge discoveryClassification
1. Prediction methods
2. Structure discovery
3. Relationship mining
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Knowledge discovery1) Prediction methods
● The goal is to develop a model which can infer a single aspect of the data ○ The predicted variable
○ Similar to dependent variables in traditional statistical analysis
● … from some combination of other aspects of the data○ Predictor variables
○ Similar to independent variables in traditional statistical analysis
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Knowledge discovery1) Prediction methods (II)
● Prediction models are commonly used: ○ Predict future events (Dekker2009; Feng2009;
MingMing2012)
○ Predict variables that are not feasible to directly collect in real-time
■ Example: collecting data on affect or engagement in
real-time often requires expensive observations or disruptive self-report measures
■ Whereas a prediction model based on student log
data can be completely non-intrusive (Sabourin2011)
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Knowledge discovery1) Prediction methods (III)
Source: http://etec.ctlt.ubc.ca/510wiki/Learning_Analytics
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Knowledge discovery1) Prediction methods (IV)
● Three types of prediction models are common in EDM/LA:○ Classifiers○ Regressors○ Latent knowledge estimation
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Knowledge discovery1) Prediction methods (V)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
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Knowledge discovery1) Prediction methods (VI)
● Classifiers○ The predicted variable can be either a binary (e.g. 0 or
1) or a categorical variable
○ Some popular classification methods in educational domains include:
■ Decision trees
■ Random forest
■ Decision rules
■ Step regression
■ Logistic regression
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Knowledge discovery1) Prediction methods (VII)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
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Knowledge discovery1) Prediction methods (VIII)
● Regressors○ The predicted variable is a continuous variable
■ For example: if the Grade can be explained by the number of pending subjects and the call number
○ The most popular regressor in EDM is linear regression
■ Note that linear regression is not used the same way in EDM/LA as in traditional statistics, despite the identical name
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Knowledge discovery1) Prediction methods (IX)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
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Knowledge discovery1) Prediction methods (X)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
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Knowledge discovery1) Prediction methods (XI)
● Latent Knowledge Estimation○ Actually is a special type of classifier
○ A student’s knowledge of specific skills and concepts is
assessed by their patterns of correctness on those skills
○ A wide range of algorithms exist for latent knowledge estimation, being the two most popular:
■ Bayesian Knowledge Tracing (Corbett & Anderson, 1995)
■ Performance Factors Analysis (Pavlik2009)
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Knowledge discovery1) Prediction methods (XII)
● Classifiers in WEKA are models for predicting nominal or numeric quantities
● Implemented learning schemes include:○ Decision trees and lists, instance-based classifiers,
support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, etc.
● “Meta”-classifiers include:○ Bagging, boosting, stacking, error-correcting output
codes, locally weighted learning, etc.
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Knowledge discovery1) Prediction methods (XIII)
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Knowledge discovery2) Structure discovery
● Attempt to find structure in the data without an a priori idea of what should be found
● It is, actually, a very different goal than in prediction○ In prediction, there is a specific variable that the
EDM/LA researcher attempts to model;
○ By contrast, there is not a specific variable of interest in structure discovery
○ Instead, the researcher attempts to determine what structure emerges naturally from the data
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Knowledge discovery2) Structure discovery (II)
● Include:○ Clustering○ Factor analysis○ Social Network Analysis○ Domain Structure Discovery
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Knowledge discovery2) Structure discovery (III)
● Clustering○ The goal is to find data points that naturally group
together, splitting the full data set into a set of clusters
○ Clustering is particularly useful in cases where the
most common categories within the data set are not known in advance
○ If a set of clusters is well-selected, each data point in a
cluster will generally be more similar to the other data points in that cluster than data points in other clusters
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Knowledge discovery2) Structure discovery (IV)
● Clustering○ Clusters have been used to group students (Beal2006)
and student actions (Amershi2009)
■ Amershi & Conati (2009) found characteristic
patterns in how students use exploratory learning
environments, and used this information to identify more and less effective student strategies
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Knowledge discovery2) Structure discovery (IV)
● Factor analysis○ A closely related method
○ Here, the goal is to find variables that naturally group
together, splitting the set of variables (as opposed to
the data points) into a set of latent (not directly observable) factors
○ Factor analysis is frequently used in psychometrics for validating or determining scales
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Knowledge discovery2) Structure discovery (V)
● Factor analysis○ In EDM/LA, factor analysis is used for dimensionality
reduction (e.g., reducing the number of variables) for a wide variety of applications
○ For instance, [Baker2009] used factor analysis to
determine which design choices are made in common by the designers of intelligent tutoring systems
■ For instance, tutor designers tend to use principle
based hints rather than concrete hints in tutor problems that have brief problem scenarios
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Knowledge discovery2) Structure discovery (VI)
● Social Network Analysis○ Models are developed of the relationships and
interactions between individual actors, as well as the
patterns that emerge from those relationships and interactions
○ Examples
■ Understanding the differences between effective and ineffective project groups [Kay2006]
■ How students’ communication behaviors change over time [Haythornthwaite2001]
■ How students’ positions in a social network relate
to their perception of being part of a learning community [Dawson2008]
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Knowledge discovery2) Structure discovery (VII)
● Domain structure discovery○ Consists of finding the structure of knowledge in an
educational domain (e.g., how specific content maps to specific knowledge components or skills, across students)
○ This could consist of mapping problems in educational software to specific knowledge components, in order to group the problems effectively for latent knowledge
estimation and problem selection [Koedinger2006], or could consist of mapping test items to skills [Tatsuoka1995]
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Knowledge discovery2) Structure discovery (VIII)
● WEKA contains “clusterers” for finding groups of similar instances in a dataset
● Implemented schemes are:○ k-Means, EM, Cobweb, X-means, FarthestFirst
● Clusters can be visualized and compared to “true” clusters (if given)
● Evaluation based on loglikelihood if clustering scheme produces a probability distribution
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Knowledge discovery3) Relationship mining
● Discover relationships between variables in a data set with a large number of variables
● It has historically been the most common category of EDM research [Baker2009]
● It may take the form of attempting to find out which variables are most strongly associated with a single variable of particular interest
● Or may take the form of attempting to discover which relationships between any two variables are strongest
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Knowledge discovery3) Relationship mining (II)
● There are four types of relationship mining○ Association rule mining○ Correlation mining○ Sequential pattern mining○ Causal data mining
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Knowledge discovery3) Relationship mining (III)
● Association rule mining○ The goal is to find if-then rules of the form that if some
set of variable values is found, another variable will generally have a specific value
○ For instance, [BenNaim2009] used association rule mining to find patterns of successful student performance in an engineering simulation, to make better suggestions to students having difficulty about how they can improve their performance
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Knowledge discovery3) Relationship mining (IV)
● Correlation mining○ The goal is to find positive or negative linear
correlations between variables (using post-hoc corrections or dimensionality reduction methods when appropriate to avoid finding spurious relationships)
○ An example can be found in [Baker2009], where correlations were computed between a range of features of the design of intelligent tutoring system lessons and students’ prevalence of gaming the system
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Knowledge discovery3) Relationship mining (V)
● Sequential pattern mining○ The goal is to find temporal associations between
events
○ One successful use of this approach was work by
[Perera2009], to determine what path of student collaboration behaviors leads to a more successful eventual group project
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Knowledge discovery3) Relationship mining (VI)
● Causal data mining○ The goal is to find whether one event (or observed
construct) was the cause of another event (or observed construct)
○ For example to predict which factors will lead a student to do poorly in a class [Fancsali2012]
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Knowledge discovery3) Relationship mining (VII)
● WEKA contains an implementation of the Apriori algorithm for learning association rules○ Works only with discrete data
● Can identify statistical dependencies between groups of attributes:○ milk, butter bread, eggs (with confidence 0.9 and
support 2000)
● Apriori can compute all rules that have a given minimum support and exceed a given confidence
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Knowledge discovery3) Relationship mining (VIII)
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Knowledge discovery4) Attribute selection
● Panel that can be used to investigate which (subsets of) attributes are the most predictive ones
● Attribute selection methods contain two parts:○ A search method: best-first, forward selection,
random, exhaustive, genetic algorithm, ranking
○ An evaluation method: correlation-based, wrapper, information gain, chi-squared, etc.
● Very flexible: WEKA allows (almost) arbitrary combinations of these two
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Knowledge discovery4) Attribute selection (II)
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Knowledge discovery4) Attribute selection (III)
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References[Amershi2009] Amershi, S., Conati, C. (2009). Combining Unsupervised and Supervised Machine Learning to Build User Models for Exploratory Learning Environments. Journal of Educational Data Mining, 1(1), 71-81.
[BakerSiemens2014] Baker, R., and George Siemens. "Educational data mining and learning analytics." Cambridge Handbook of the Learning Sciences: (2014).
[BakerYacef2009] Baker, R.S.J.d., Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), 3-17
[Beal2006] Beal, C.R., Qu, L., & Lee, H. (2006). Classifying learner engagement through integration of multiple data sources. Paper presented at the 21st National Conference on Artificial Intelligence (AAAI-2006), Boston, MA.
[CorbettAnderson1995] Corbett, A.T., Anderson, J.R. (1995). Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.
[Dawson2008] Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224-238.
[Dekker2009] Dekker, G., Pechenizkiy, M., and Vleeshouwers, J. (2009). Predicting students drop out: A case study. Proceedings of the 2nd International Conference on Educational Data Mining, EDM'09, 41-50
[Fancsali2012] Fancsali, S. (2012) Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial Results. Proceedings of the 5th International Conference on Educational Data Mining, 238-239.
[Feng2009] Feng, M., Heffernan, N., & Koedinger, K. (2009). Addressing the Assessment Challenge in an Intelligent Tutoring System that Tutors as it Assesses. User Modeling and User-Adapted Interaction, 19, 243-266
[Ferguson2012] Ferguson, R. (2012). The State Of Learning Analytics in 2012: A Review and Future Challenges. Technical Report KMI-12-01, Knowledge Media Institute, The Open University, UK. http://kmi.open.ac.uk/publications/techreport/kmi-12-01
[Ferguson2014] Learning analytics FAQs [Online]. URL: http://www.slideshare.net/R3beccaF/learning-analytics-fa-qs
[Haythornthwaite2001] Haythornthwaite, C. (2001). Exploring Multiplexity: Social Network Structures in a ComputerSupported Distance Learning Class. The Information Society: An International Journal, 17 (3), 211-226.
[Kay2006] Kay, J., Maisonneuve, N., Yacef, K., Reimann, P. (2006) The Big Five and Visualisations of Team Work Activity. Proceedings of the International Conference on Intelligent Tutoring Systems, 197 – 206.
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Data Analytics process in Learning and Academic
Analytics projects
Day 3: Data processing
Alex Rayón [email protected]
DeustoTech Learning – Deusto Institute of Technology – University of DeustoAvda. Universidades 24, 48007 Bilbao, Spain
www.deusto.es