PUB - Publikationen an der Universität Bielefeld

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• [69]
  

  2024 | Zeitschriftenaufsatz | PUB-ID: 2987814

  Morgenroth T, Begeny CT, Kirby TA, Paaßen B, Zeng Y. Dissecting Whiteness: consistencies and differences in the stereotypes of lower- and upper-class White US Americans. Self and Identity. 2024:1-25.

  PUB | DOI | WoS
   
• [68]
  

  2023 | Preprint | Veröffentlicht | PUB-ID: 2980970

  Strotherm J, Müller A, Hammer B, Paaßen B. Fairness in KI-Systemen. 2023.

  PUB | Download (ext.) | arXiv
   
• [67]
  

  2022 | Konferenzbeitrag | PUB-ID: 2979001

  Paaßen B, Dywel M, Fleckenstein M, Pinkwart N. Sparse Factor Autoencoders for Item Response Theory. In: Cristea AI, Brown C, Mitrovic T, Bosch N, eds. Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). 2022: 17–26.

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• [66]
  

  2022 | Zeitschriftenaufsatz | PUB-ID: 2978970

  Paaßen B, Koprinska I, Yacef K. Recursive Tree Grammar Autoencoders. Machine Learning. 2022;111:3393–3423.

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• [65]
  

  2022 | Zeitschriftenaufsatz | PUB-ID: 2979004

  Paaßen B, Dehne J, Krishnaraja S, Kovalkov A, Gal K, Pinkwart N. A conceptual graph-based model of creativity in learning. Frontiers in Education. 2022;7.

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• [64]
  

  2022 | Konferenzbeitrag | PUB-ID: 2979003

  Paaßen B, Baumgartner T, Geisen M, Riedl N, Kravčík M. Few-shot Keypose Detection for Learning of Psychomotor Skills. In: Asyraaf Mat Sanusi K, Limbu B, Schneider J, Di Mitri D, Klemke R, eds. Proceedings of the Second International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2022). 2022: 22–27.

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• [63]
  

  2022 | Konferenzbeitrag | PUB-ID: 2979002

  Paaßen B, Dywel M, Fleckenstein M, Pinkwart N. Interpretable Knowledge Gain Prediction for Vocational Preparatory E-Learnings. In: DeFalco JA, Matos DDM da C, Blanc B, Reichow I, eds. Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022) Practitioner’s Track. 2022: 132–137.

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• [62]
  

  2022 | Konferenzbeitrag | PUB-ID: 2979000

  Paaßen B, Göpfert C, Pinkwart N. Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood. In: Cristea AI, Brown C, Mitrovic T, Bosch N, eds. Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). 2022: 555–559.

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• [61]
  

  2022 | Konferenzbeitrag | PUB-ID: 2978999

  Picones G, Paaßen B, Koprinska I, Yacef K. Combining domain modelling and student modelling techniques in a single pipeline to support task-sequencing. In: Cristea AI, Brown C, Mitrovic T, Bosch N, eds. Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). 2022: 217–227.

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• [60]
  

  2022 | Zeitschriftenaufsatz | PUB-ID: 2978998

  Paaßen B, Schulz A, C. Stewart T, Hammer B. Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems. 2022;33(6):2575–2585.

  PUB | DOI | Download (ext.) | arXiv
   
• [59]
  

  2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2978969

  Paaßen B, McBroom J, Jeffries B, Koprinska I, Yacef K. Mapping Python Programs to Vectors using Recursive Neural Encodings. Journal of Educational Datamining. 2021;13(3):1–35.

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• [58]
  

  2021 | Zeitschriftenaufsatz | PUB-ID: 2978997

  Kovalkov A, Paaßen B, Segal A, Pinkwart N, Gal K. Automatic Creativity Measurement in Scratch Programs Across Modalities. IEEE Transactions on Learning Technologies. 2021;14(6):740–753.

  PUB | DOI | Download (ext.) | arXiv
   
• [57]
  

  2021 | Konferenzbeitrag | PUB-ID: 2978996

  Bacciu D, Bianchi FM, Paaßen B, Alippi C. Deep learning for graphs. In: Verleysen M, ed. {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)}. 2021: 89–98.

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• [56]
  

  2021 | Konferenzbeitrag | PUB-ID: 2978995

  Paaßen B, Kravčík M. Teaching psychomotor skills using machine learning for error detection. In: Klemke R, Asyraaf Mat Sanusi K, eds. Proceedings of the 1st International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2021). 2021: 8–14.

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• [55]
  

  2021 | Konferenzbeitrag | PUB-ID: 2978967

  Paaßen B. An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs. In: Reyes N, Connor R, Kriege N, et al., eds. Proceedings of the 14th International Conference on Similarity Search and Applications (SISAP 2021). Springer; 2021: 364–371.

  PUB | DOI | Download (ext.) | arXiv
   
• [54]
  

  2021 | Konferenzbeitrag | PUB-ID: 2978966

  Kovalkov A, Paaßen B, Segal A, Gal K, Pinkwart N. Modeling Creativity in Visual Programming: From Theory to Practice. In: Bouchet F, Vie J-J, Hsiao S, Sahebi S, eds. Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021). International Educational Datamining Society; 2021.

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• [53]
  

  2021 | Konferenzbeitrag | PUB-ID: 2978965

  Paaßen B, Bertsch A, Langer-Fischer K, et al. Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks. In: Bouchet F, Vie J-J, Hsiao S, Sahebi S, eds. Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021). International Educational Datamining Society; 2021.

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• [52]
  

  2021 | Konferenzbeitrag | PUB-ID: 2978964

  McBroom J, Paaßen B, Jeffries B, Koprinska I, Yacef K. Progress Networks as a Tool for Analysing Student Programming Difficulties. In: Szabo C, Sheard J, eds. Proceedings of the Twenty-Third Australasian Computing Education Conference (ACE '21). Association for Computing Machinery; 2021: 158–167.

  PUB | DOI
   
• [51]
  

  2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542

  Paaßen B, Schulz A, Hammer B. Reservoir Stack Machines. Neurocomputing. 2021;470:352-364.

  PUB | DOI | Download (ext.) | WoS | arXiv
   
• [50]
  

  2020 | Konferenzbeitrag | PUB-ID: 2978963

  Paaßen B, Koprinska I, Yacef K. Tree Echo State Autoencoders with Grammars. In: Roy A, ed. Proceedings of the 2020 International Joint Conference on Neural Networks ({IJCNN} 2020). 2020: 1–8.

  PUB | DOI | Download (ext.) | arXiv
   
• [49]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941931

  Paaßen B, Schulz A. Reservoir memory machines. In: Verleysen M, ed. Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020). Bruges: i6doc; 2020: 567-572.

  PUB | Download (ext.) | arXiv
   
• [48]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2944191

  Morgenroth T, Stratemeyer M, Paaßen B. The Gendered Nature and Malleability of Gamer Stereotypes. Cyberpsychology, Behavior, and Social Networking. 2020;23(8):557-561.

  PUB | DOI | WoS | PubMed | Europe PMC
   
• [47]
  

  2019 | Monographie | PUB-ID: 2935200

  Paaßen B, Artelt A, Hammer B. Lecture Notes on Applied Optimization. Faculty of Technology, Bielefeld University; 2019.

  PUB | Dateien verfügbar
   
• [46]
  

  2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458

  Prahm C, Schulz A, Paaßen B, et al. Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2019;27(5):956-962.

  PUB | PDF | DOI | WoS | PubMed | Europe PMC
   
• [45]
  

  2019 | Datenpublikation | PUB-ID: 2941052

  Paaßen B. Python Programming Dataset. Bielefeld University; 2019.

  PUB | Dateien verfügbar | DOI
   
• [44]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937053

  Paaßen B. Adversarial Edit Attacks for Tree Data. In: Yin H, Camacho D, Tino P, eds. Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019). Lecture Notes in Computer Science. Vol 11871. Cham: Springer; 2019: 359-366.

  PUB | DOI | Download (ext.) | arXiv
   
• [43]
  

  2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2935953

  Price TW, Dong Y, Zhi R, et al. A Comparison of the Quality of Data-Driven Programming Hint Generation Algorithms. International Journal of Artificial Intelligence in Education. 2019;29(3):368-395.

  PUB | DOI | WoS
   
• [42]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933502

  Paaßen B, Bunge A, Hainke C, Sindelar L, Vogelsang M. Dynamic fairness - Breaking vicious cycles in automatic decision making. In: Verleysen M, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Louvain-la-Neuve: i6doc; 2019: 477-482.

  PUB | Download (ext.) | arXiv
   
• [41]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2934571

  Paaßen B, Gallicchio C, Micheli A, Sperduti A. Embeddings and Representation Learning for Structured Data. In: Verleysen M, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). 2019: 85-94.

  PUB | Download (ext.) | arXiv
   
• [40]
  

  2019 | Bielefelder E-Dissertation | PUB-ID: 2935545

  Paaßen B. Metric Learning for Structured Data. Bielefeld: Universität Bielefeld; 2019.

  PUB | PDF | DOI
   
• [39]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900

  Paaßen B, Göpfert C, Hammer B. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters. 2018;48(2):669-689.

  PUB | DOI | Download (ext.) | WoS | arXiv
   
• [38]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505

  Paaßen B, Schulz A, Hahne J, Hammer B. Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing. 2018;298:122-133.

  PUB | DOI | Download (ext.) | WoS | arXiv
   
• [37]
  

  2018 | Datenpublikation | PUB-ID: 2916863

  Paaßen B, Ahmaro A. VBB Shortest Path Data 2018. Bielefeld University; 2018.

  PUB | Dateien verfügbar | DOI
   
• [36]
  

  2018 | Datenpublikation | PUB-ID: 2919994

  Paaßen B. Tree Edit Distance Learning via Adaptive Symbol Embeddings. Bielefeld University; 2018.

  PUB | Dateien verfügbar | DOI
   
• [35]
  

  2018 | Datenpublikation | PUB-ID: 2916990

  Paaßen B. Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University; 2018.

  PUB | Dateien verfügbar | DOI
   
• [34]
  

  2018 | Datenpublikation | PUB-ID: 2916980

  Paaßen B. Relational Neural Gas. Bielefeld University; 2018.

  PUB | Dateien verfügbar | DOI
   
• [33]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389

  Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N. The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining. 2018;10(1):1-35.

  PUB | Download (ext.) | arXiv
   
• [32]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844

  Paaßen B, Gallicchio C, Micheli A, Hammer B. Tree Edit Distance Learning via Adaptive Symbol Embeddings. In: Dy J, Krause A, eds. Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Proceedings of Machine Learning Research. Vol 80. 2018: 3973-3982.

  PUB | Download (ext.) | arXiv
   
• [31]
  

  2018 | Konferenzbeitrag | PUB-ID: 2916318

  Berger K, Schulz A, Paaßen B, Hammer B. Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier. Presented at the SSCI CIDM 2017.

  PUB | DOI
   
• [30]
  

  2018 | Preprint | Entwurf | PUB-ID: 2919918

  Paaßen B. Revisiting the tree edit distance and its backtracing: A tutorial. arXiv:1805.06869. Draft.

  PUB | Download (ext.) | arXiv
   
• [29]
  

  2017 | Datenpublikation | PUB-ID: 2913104

  Paaßen B. Time Series Prediction for Relational and Kernel Data. Bielefeld University; 2017.

  PUB | Dateien verfügbar | DOI
   
• [28]
  

  2017 | Datenpublikation | PUB-ID: 2912671

  Paaßen B, Schulz A. Linear Supervised Transfer Learning Toolbox. Bielefeld University; 2017.

  PUB | Dateien verfügbar | DOI
   
• [27]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369

  Paaßen B, Schulz A, Hahne J, Hammer B. An EM transfer learning algorithm with applications in bionic hand prostheses. In: Verleysen M, ed. Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Bruges: i6doc.com; 2017: 129-134.

  PUB | PDF
   
• [26]
  

  2017 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2914663

  Paaßen B. Two or three things we do (not) know about distances. In: Schleif F-M, Villmann T, eds. Proceedings of the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017). Machine Learning Reports. 2017: 32-33.

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• [25]
  

  2017 | Datenpublikation | PUB-ID: 2913083

  Paaßen B. BinaryAdder UML Dataset. Bielefeld University; 2017.

  PUB | Dateien verfügbar | DOI
   
• [24]
  

  2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905302

  Paaßen B, Morgenroth T, Stratemeyer M. What is a True Gamer? The Male Gamer Stereotype and the Marginalization of Women in Video Game Culture. Sex Roles. 2017;76(7-8):421-435.

  PUB | PDF | DOI | WoS
   
• [23]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037

  Prahm C, Schulz A, Paaßen B, Aszmann O, Hammer B, Dorffner G. Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In: ten Telje A, Popow C, Holmes JH, Sacchi L, eds. Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). Lecture Notes in Computer Science. Vol 10259. Springer; 2017: 338--342.

  PUB | Dateien verfügbar | DOI
   
• [22]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367

  Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B. Local Reject Option for Deterministic Multi-class SVM. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 251--258.

  PUB | DOI
   
• [21]
  

  2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2783224

  Paaßen B, Mokbel B, Hammer B. Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing. 2016;192(SI):3-13.

  PUB | PDF | DOI | WoS
   
• [20]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676

  Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2016: 41--46.

  PUB | PDF
   
• [19]
  

  2016 | Datenpublikation | PUB-ID: 2900684

  Paaßen B. Java Sorting Programs. Bielefeld University; 2016.

  PUB | Dateien verfügbar | DOI
   
• [18]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509

  Paaßen B, Jensen J, Hammer B. Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. In: Barnes T, Chi M, Feng M, eds. Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, North Carolina, USA: International Educational Datamining Society; 2016: 183-190.

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• [17]
  

  2016 | Datenpublikation | PUB-ID: 2900666

  Paaßen B. MiniPalindrome. Bielefeld University; 2016.

  PUB | Dateien verfügbar | DOI
   
• [16]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729

  Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.

  PUB | PDF | DOI
   
• [15]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855

  Paaßen B, Schulz A, Hammer B. Linear Supervised Transfer Learning for Generalized Matrix LVQ. In: Hammer B, Martinetz T, Villmann T, eds. Proceedings of the Workshop New Challenges in Neural Computation 2016. Machine Learning Reports. 2016: 11-18.

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• [14]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178

  Prahm C, Paaßen B, Schulz A, Hammer B, Aszmann O. Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift. In: Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL, eds. Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Springer; 2016: 153--157.

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• [13]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031

  Mokbel B, Paaßen B, Schleif F-M, Hammer B. Metric learning for sequences in relational LVQ. Neurocomputing. 2015;169(SI):306-322.

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• [12]
  

  2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2724156

  Paaßen B, Mokbel B, Hammer B. Adaptive structure metrics for automated feedback provision in Java programming. In: Verleysen M, ed. Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2015: 307-312.

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• [11]
  

  2015 | Report | PUB-ID: 2712107

  Stöckel A, Paaßen B, Dickfelder R, et al. SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. bioRxive.org; 2015.

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• [10]
  

  2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2762087

  Paaßen B, Mokbel B, Hammer B. A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems. In: Santos OC, Boticario JG, Romero C, et al., eds. Proceedings of the 8th International Conference on Educational Data Mining. International Educational Datamining Society; 2015: 632-632.

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• [9]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955

  Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B. Autonomous Learning of Representations. KI - Künstliche Intelligenz. 2015;29(4):339–351.

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• [8]
  

  2015 | Bielefelder Masterarbeit | PUB-ID: 2736686

  Paaßen B. Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming. Bielefeld: Bielefeld University; 2015.

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• [7]
  

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214

  Hofmann D, Schleif F-M, Paaßen B, Hammer B. Learning interpretable kernelized prototype-based models. Neurocomputing. 2014;141:84-96.

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• [6]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554

  Mokbel B, Paaßen B, Hammer B. Adaptive distance measures for sequential data. In: Verleysen M, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com; 2014: 265-270.

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• [5]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760

  Paaßen B, Stöckel A, Dickfelder R, et al. Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. In: Maynard D, Erp van M, Davis B, eds. Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING). Dublin, Ireland; 2014: 25-32.

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• [4]
  

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2734058

  Gross S, Mokbel B, Paaßen B, Hammer B, Pinkwart N. Example-based feedback provision using structured solution spaces. International Journal of Learning Technology. 2014;9(3):248-280.

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• [3]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067

  Mokbel B, Paaßen B, Hammer B. Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In: Wermter S, Weber C, Duch W, et al., eds. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. Vol 8681. Springer; 2014: 571-578.

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• [2]
  

  2013 | Datenpublikation | PUB-ID: 2692491

  Paaßen B. VBB Midi Dataset. Bielefeld University; 2013.

  PUB | Dateien verfügbar | DOI
   
• [1]
  

  2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625185

  Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B. Domain-Independent Proximity Measures in Intelligent Tutoring Systems. In: D'Mello SK, Calvo RA, Olney A, eds. Proceedings of the 6th International Conference on Educational Data Mining (EDM). 2013: 334-335.

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