Data Analytics process in
Learning and Academic
Analytics projects
Day 3: Data processing
Alex Rayón Jerez
alex.rayon@deus...
Table of contents
● Data dimensions
● Applications
● Data processing in an ETL refined data
● Knowledge discovery
Table of contents
● Data dimensions
● Applications
● Data processing in an ETL refined data
● Knowledge discovery
Data dimensions
Summary
[Verbert2011]
Data dimensions
1) Computing
● Software
○ Example
■ Q1. Among the tools, which is more representative of
the final grade?
...
Data dimensions
2) Location
● Quantitative
● Qualitative
Data dimensions
3) Time
● Timestamp
● Time interval
Data dimensions
4) Activity
● Events
● Tasks
● Goals
● Subject
○ Example
■ Q2. Which are the differences in terms of grade...
Data dimensions
5) Physical condition
● Noise level
● Lighting
● ...
Data dimensions
6) Resource
● Physical resource
● Virtual resource
Data dimensions
7) User
● Basic info
○ Example
■ Q3. Is there any gender difference in the use of the
tools?
● Knowledge
●...
Data dimensions
8) Relations
● Social relations
○ Example
■ Q4. Are there groups of people that repeatedly
collaborate in ...
Table of contents
● Data dimensions
● Applications
● Data processing in an ETL refined data
● Knowledge discovery
Applications
Why do learners use analytics?
[Ferguson2014]
● Monitor their own activities and interactions
● Monitor the l...
Applications
Why do teachers use analytics?
[Ferguson2014]
● Monitor the learning process
● Explore student data
● Identif...
Applications
Why do teachers use analytics? (Ii)
● Increase awareness, reflect and self reflect
● Increase understanding o...
Table of contents
● Data dimensions
● Applications
● Data processing in an ETL refined data
● Knowledge discovery
Data processing
Transform menu
Data processing
Scripting menu
Data processing
Joins menu
Data processing
Statistics menu
Data processing
WEKA plugin
Data processing
WEKA plugin (II)
Data processing
WEKA plugin (III)
Data processing
WEKA plugin (IV)
Data processing
WEKA plugin (V)
Table of contents
● Data dimensions
● Applications
● Data processing in an ETL refined data
● Knowledge discovery
Knowledge discovery
Introduction
[BakerSiemens2014]
This review draws on past reviews (cf. Baker & Yacef, 2009; Romero & V...
Knowledge discovery
Introduction (II)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/datamin...
Knowledge discovery
Classification
1. Prediction methods
2. Structure discovery
3. Relationship mining
Knowledge discovery
1) Prediction methods
● The goal is to develop a model which can infer
a single aspect of the data
○ T...
Knowledge discovery
1) Prediction methods (II)
● Prediction models are commonly used:
○ Predict future events (Dekker2009;...
Knowledge discovery
1) Prediction methods (III)
Source: http://etec.ctlt.ubc.ca/510wiki/Learning_Analytics
Knowledge discovery
1) Prediction methods (IV)
● Three types of prediction models are common
in EDM/LA:
○ Classifiers
○ Re...
Knowledge discovery
1) Prediction methods (V)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc...
Knowledge discovery
1) Prediction methods (VI)
● Classifiers
○ The predicted variable can be either a binary (e.g. 0 or
1)...
Knowledge discovery
1) Prediction methods (VII)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mo...
Knowledge discovery
1) Prediction methods (VIII)
● Regressors
○ The predicted variable is a continuous variable
■ For exam...
Knowledge discovery
1) Prediction methods (IX)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/moo...
Knowledge discovery
1) Prediction methods (X)
Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc...
Knowledge discovery
1) Prediction methods (XI)
● Latent Knowledge Estimation
○ Actually is a special type of classifier
○ ...
Knowledge discovery
1) Prediction methods (XII)
● Classifiers in WEKA are models for predicting
nominal or numeric quantit...
Knowledge discovery
1) Prediction methods (XIII)
Knowledge discovery
2) Structure discovery
● Attempt to find structure in the data without
an a priori idea of what should...
Knowledge discovery
2) Structure discovery (II)
● Include:
○ Clustering
○ Factor analysis
○ Social Network Analysis
○ Doma...
Knowledge discovery
2) Structure discovery (III)
● Clustering
○ The goal is to find data points that naturally group
toget...
Knowledge discovery
2) Structure discovery (IV)
● Clustering
○ Clusters have been used to group students (Beal2006)
and st...
Knowledge discovery
2) Structure discovery (IV)
● Factor analysis
○ A closely related method
○ Here, the goal is to find v...
Knowledge discovery
2) Structure discovery (V)
● Factor analysis
○ In EDM/LA, factor analysis is used for dimensionality
r...
Knowledge discovery
2) Structure discovery (VI)
● Social Network Analysis
○ Models are developed of the relationships and
...
Knowledge discovery
2) Structure discovery (VII)
● Domain structure discovery
○ Consists of finding the structure of knowl...
Knowledge discovery
2) Structure discovery (VIII)
● WEKA contains “clusterers” for finding groups
of similar instances in ...
Knowledge discovery
3) Relationship mining
● Discover relationships between variables in a
data set with a large number of...
Knowledge discovery
3) Relationship mining (II)
● There are four types of relationship mining
○ Association rule mining
○ ...
Knowledge discovery
3) Relationship mining (III)
● Association rule mining
○ The goal is to find if-then rules of the form...
Knowledge discovery
3) Relationship mining (IV)
● Correlation mining
○ The goal is to find positive or negative linear
cor...
Knowledge discovery
3) Relationship mining (V)
● Sequential pattern mining
○ The goal is to find temporal associations bet...
Knowledge discovery
3) Relationship mining (VI)
● Causal data mining
○ The goal is to find whether one event (or observed
...
Knowledge discovery
3) Relationship mining (VII)
● WEKA contains an implementation of the
Apriori algorithm for learning a...
Knowledge discovery
3) Relationship mining (VIII)
Knowledge discovery
4) Attribute selection
● Panel that can be used to investigate which
(subsets of) attributes are the m...
Knowledge discovery
4) Attribute selection (II)
Knowledge discovery
4) Attribute selection (III)
References
[Amershi2009] Amershi, S., Conati, C. (2009). Combining Unsupervised and Supervised Machine Learning to Build U...
References (II)
[Koedinger2006] Koedinger, K. R., & Corbett, A. T. (2006). Cognitive Tutors: Technology bringing learning ...
Data Analytics process in
Learning and Academic
Analytics projects
Day 3: Data processing
Alex Rayón Jerez
alex.rayon@deus...
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Data Analytics.03. Data processing

  1. 1. Data Analytics process in Learning and Academic Analytics projects Day 3: Data processing Alex Rayón Jerez alex.rayon@deusto.es DeustoTech Learning – Deusto Institute of Technology – University of Deusto Avda. Universidades 24, 48007 Bilbao, Spain www.deusto.es
  2. 2. Table of contents ● Data dimensions ● Applications ● Data processing in an ETL refined data ● Knowledge discovery
  3. 3. Table of contents ● Data dimensions ● Applications ● Data processing in an ETL refined data ● Knowledge discovery
  4. 4. Data dimensions Summary [Verbert2011]
  5. 5. Data dimensions 1) 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
  6. 6. Data dimensions 2) Location ● Quantitative ● Qualitative
  7. 7. Data dimensions 3) Time ● Timestamp ● Time interval
  8. 8. Data dimensions 4) 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?
  9. 9. Data dimensions 5) Physical condition ● Noise level ● Lighting ● ...
  10. 10. Data dimensions 6) Resource ● Physical resource ● Virtual resource
  11. 11. Data dimensions 7) 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
  12. 12. Data dimensions 8) 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
  13. 13. Table of contents ● Data dimensions ● Applications ● Data processing in an ETL refined data ● Knowledge discovery
  14. 14. Applications Why 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!
  15. 15. Applications Why 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
  16. 16. Applications Why 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
  17. 17. Table of contents ● Data dimensions ● Applications ● Data processing in an ETL refined data ● Knowledge discovery
  18. 18. Data processing Transform menu
  19. 19. Data processing Scripting menu
  20. 20. Data processing Joins menu
  21. 21. Data processing Statistics menu
  22. 22. Data processing WEKA plugin
  23. 23. Data processing WEKA plugin (II)
  24. 24. Data processing WEKA plugin (III)
  25. 25. Data processing WEKA plugin (IV)
  26. 26. Data processing WEKA plugin (V)
  27. 27. Table of contents ● Data dimensions ● Applications ● Data processing in an ETL refined data ● Knowledge discovery
  28. 28. Knowledge discovery Introduction [BakerSiemens2014] This review draws on past reviews (cf. Baker & Yacef, 2009; Romero & Ventura, 2010; Ferguson, 2012; Siemens & Baker, 2012)
  29. 29. Knowledge discovery Introduction (II) Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
  30. 30. Knowledge discovery Classification 1. Prediction methods 2. Structure discovery 3. Relationship mining
  31. 31. Knowledge discovery 1) 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
  32. 32. Knowledge discovery 1) 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)
  33. 33. Knowledge discovery 1) Prediction methods (III) Source: http://etec.ctlt.ubc.ca/510wiki/Learning_Analytics
  34. 34. Knowledge discovery 1) Prediction methods (IV) ● Three types of prediction models are common in EDM/LA: ○ Classifiers ○ Regressors ○ Latent knowledge estimation
  35. 35. Knowledge discovery 1) Prediction methods (V) Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
  36. 36. Knowledge discovery 1) 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
  37. 37. Knowledge discovery 1) Prediction methods (VII) Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
  38. 38. Knowledge discovery 1) 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
  39. 39. Knowledge discovery 1) Prediction methods (IX) Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
  40. 40. Knowledge discovery 1) Prediction methods (X) Source: Data Mining with WEKA MOOC (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
  41. 41. Knowledge discovery 1) 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)
  42. 42. Knowledge discovery 1) 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.
  43. 43. Knowledge discovery 1) Prediction methods (XIII)
  44. 44. Knowledge discovery 2) 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
  45. 45. Knowledge discovery 2) Structure discovery (II) ● Include: ○ Clustering ○ Factor analysis ○ Social Network Analysis ○ Domain Structure Discovery
  46. 46. Knowledge discovery 2) 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
  47. 47. Knowledge discovery 2) 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
  48. 48. Knowledge discovery 2) 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
  49. 49. Knowledge discovery 2) 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
  50. 50. Knowledge discovery 2) 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
  51. 51. Knowledge discovery 2) 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]
  52. 52. Knowledge discovery 2) 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
  53. 53. Knowledge discovery 3) 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
  54. 54. Knowledge discovery 3) Relationship mining (II) ● There are four types of relationship mining ○ Association rule mining ○ Correlation mining ○ Sequential pattern mining ○ Causal data mining
  55. 55. Knowledge discovery 3) 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
  56. 56. Knowledge discovery 3) 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
  57. 57. Knowledge discovery 3) 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
  58. 58. Knowledge discovery 3) 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]
  59. 59. Knowledge discovery 3) 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
  60. 60. Knowledge discovery 3) Relationship mining (VIII)
  61. 61. Knowledge discovery 4) 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
  62. 62. Knowledge discovery 4) Attribute selection (II)
  63. 63. Knowledge discovery 4) Attribute selection (III)
  64. 64. 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.
  65. 65. References (II) [Koedinger2006] Koedinger, K. R., & Corbett, A. T. (2006). Cognitive Tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.) The Cambridge Handbook of the Learning Sciences (pp. 61-78). New York: Cambridge University Press. [MingMing2012] Ming, N.C., Ming, V.L. (2012). Predicting Student Outcomes from Unstructured Data. Proceedings of the 2nd International Workshop on Personalization Approaches in Learning Environments, 11-16. [Pavlik2009] Pavlik, P.I., Cen, H., Koedinger, K.R. (2009). Performance Factors Analysis -- A New Alternative to Knowledge Tracing. Proceedings of AIED2009. [Perera2009] Perera, D., Kay, J., Koprinska, I., Yacef, K., and Zaiane, O.R. (2009). Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Transactions on Knowledge and Data Engineering, 21(6), 759-772 [RomeroVentura2010]Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state-ofthe-art. IEEE Transaction on Systems, Man and Cybernetics, part C: Applications and Reviews, 40(6), 610–618 [Sabourin2011] Sabourin, J., Rowe, J., Mott, B., Lester, J. (2011). When Off-Task in On-Task: The Affective Role of Off-Task Behavior in Narrative- Centered Learning Environments. Proceedings of the 15th International Conference on Artificial Intelligence in Education, 534-536. [SiemensBaker2012] Siemens, G., Baker, R.S.J.d. (2012). Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. [Tatsuoka1995] Tatsuoka, K.K. (1995). Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. In P. D. Nichols, S. F. Chipman, & R. L. Brennan (Eds.), Cognitively diagnostic assessment, 327–359. Hillsdale NJ: Erlbaum [Verbert2011] Dataset-driven research to improve TEL recommender systems [Online]. URL: http://www.slideshare.net/kverbert/datasetdriven- research-to-improve-tel-recommender-systems
  66. 66. Data Analytics process in Learning and Academic Analytics projects Day 3: Data processing Alex Rayón Jerez alex.rayon@deusto.es DeustoTech Learning – Deusto Institute of Technology – University of Deusto Avda. Universidades 24, 48007 Bilbao, Spain www.deusto.es

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