Handbook of Learning Analytics

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Arnold, K. E., Lonn, S., & Pistilli, M. D. (2014). An exercise in institutional reflection: The learning analytics readiness instrument (LARI). Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 163–165). New York: ACM. doi:10.1145/2567574.2567621

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Baker, R., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61–75). Springer.

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Bakharia, A., Kitto, K., Pardo, A., Gašević, D., & Dawson, S. (2016). Recipe for success: Lessons learnt from using xAPI within the connected learning analytics toolkit. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 378–382). New York: ACM.

Barker-Plummer, D., Cox, R., & Dale, R. (2009). Dimensions of difficulty in translating natural language into first order logic. In T. Barnes, M. Desmarais, C. Romero, & S. Ventura (Eds.), Proceedings of the 2nd International Conference on Educational Data Mining (EDM2009), 1–3 July 2009, Cordoba, Spain (pp. 220–229). International Educational Data Mining Society.

Barker-Plummer, D., Cox, R., & Dale, R. (2011). Student translations of natural language into logic: The grade grinder corpus release 1.0. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th Annual Conference on Educational Data Mining (EDM2011), 6–8 July 2011, Eindhoven, The Netherlands (pp. 51–60). International Educational Data Mining Society.

Beheshitha, S. S., Hatala, M., Gašević, D., & Joksimović, S. (2016). The role of achievement goal-orientations when studying effect of learning analytics visualisations. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 54–63). New York: ACM.

Ben-Naim, D., Bain, M., & Marcus, N. (2009). A user-driven and data-driven approach for supporting teachers in reflection and adaptation of adaptive tutorials. In T. Barnes, M. Desmarais, C. Romero, & S. Ventura (Eds.), Proceedings of the 2nd International Conference on Educational Data Mining (EDM2009), 1–3 July 2009, Cordoba, Spain (pp. 21–30). International Educational Data Mining Society.

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Buckingham Shum, S., Sandor, A., Goldsmith, R., Wang, X., Bass, R., & McWilliams, M. (2016). Reflecting on reflective writing analytics: Assessment challenges and iterative evaluation of a prototype tool. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 213–222). New York: ACM.

Bull, S., & Kay, J. (2016). SMILI☺ : A framework for interfaces to learning data in open learner models, learning analytics and related fields. International Journal of Artificial Intelligence in Education, 26(1), 293–331.

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Charleer, S., Klerkx, J., & Duval, E. (2015). Exploring inquiry-based learning analytics through interactive surfaces. Proceedings of the Workshop on Visual Aspects of Learning Analytics (VISLA ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA. http://ceur-ws.org/Vol-1518/paper6.pdf

Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 134–138). New York: ACM. doi:10.1145/2330601.2330636

Clow, D. (2014). Data wranglers: Human interpreters to help close the feedback loop. Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 49–53). New York: ACM. doi:10.1145/2567574.2567603

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Corrin, L., & de Barba, P. (2015). How do students interpret feedback delivered via dashboards? Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 430–431). New York: ACM. doi:10.1145/2723576.2723662

Crossley, S., Allen, L. K., Snow, E. L., & McNamara, D. S. (2015). Pssst… textual features… there is more to automatic essay scoring than just you! Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 203–207). New York: ACM. doi:10.1145/2723576.2723595

Crossley, S., Kyle, K., McNamara, D. S., & Allen, L. (2014). The importance of grammar and mechanics in writing assessment and instruction: Evidence from data mining. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (EDM2014), 4–7 July, London, UK (pp. 300–303). International Educational Data Mining Society.

Dawson, S. (2010). “Seeing” the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736–752.

Dawson, S., Bakharia, A., & Heathcote, E. (2010). SNAPP: Realising the affordances of real-time SNA within networked learning environments. In L. Dirckinck-Holmfeld, V. Hodgson, C. Jones, M. de Laat, D. McConnell, & T. Ryberg (Eds.), Proceedings of the 7th International Conference on Networked Learning (NLC 2010), 3–4 May 2010, Aalborg, Denmark. http://www.networkedlearningconference.org.uk/past/nlc2010/abstracts/PDFs/Dawson.pdf

De Liddo, A., Buckingham Shum, S., & Quinto, I. (2011). Discourse-centric learning analytics. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK ʼ11), 27 February–1 March 2011, Banff, AB, Canada (pp. 23–33). New York: ACM.

Deakin Crick, R., Huang, S., Ahmed Shafi, A., & Goldspink, C. (2015). Developing resilient agency in learning: The internal structure of learning power. British Journal of Educational Studies, 63(2), 121–160. doi:10.1080/00071005.2015.1006574

DeFalco, J. A., Baker, R., & D’Mello, S. K. (2014). Addressing behavioral disengagement in online learning. Design recommendations for intelligent tutoring systems (Vol. 2, pp. 49–56). Orlando, FL: U.S. Army Research Laboratory.

Dominguez, A. K., Yacef, K., & Curran, J. R. (2010). Data mining for generating hints in a python tutor. In R. S. J. d. Baker, A. Merceron, & P. I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (EDM2010), 11–13 June 2010, Pittsburgh, PA, USA (pp. 91–100). International Educational Data Mining Society.

Eagle, M., & Barnes, T. (2013). Evaluation of automatically generated hint feedback. In S. K. DʼMello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM2013), 6–9 July 2013, Memphis, TN, USA (pp. 372–374). International Educational Data Mining Society/Springer.

Eagle, M., & Barnes, T. (2014). Data-driven feedback beyond next-step hints. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (EDM2014), 4–7 July, London, UK (pp. 444–446). International Educational Data Mining Society.

Evans, C. (2013). Making sense of assessment feedback in higher education. Review of Educational Research, 83(1), 70–120. doi:10.3102/0034654312474350

Ezen-Can, A., & Boyer, K. E. (2013). Unsupervised classification of student dialogue acts with query-likelihood clustering. In S. K. DʼMello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM2013), 6–9 July 2013, Memphis, TN, USA (pp. 20–27). International Educational Data Mining Society/Springer.

Ezen-Can, A., & Boyer, K. E. (2015). Choosing to interact: Exploring the relationship between learner personality, attitudes, and tutorial dialogue participation. In O. C. Santos et al. (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM2015), 26–29 June 2015, Madrid, Spain (pp. 125–128). International Educational Data Mining Society.

Fancsali, S. E. (2014). Causal discovery with models: Behavior, affect, and learning in cognitive tutor algebra. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (EDM2014), 4–7 July, London, UK (pp. 28–35). International Educational Data Mining Society.

Feng, M., Beck, J. E., & Heffernan, N. T. (2009). Using learning decomposition and bootstrapping with randomization to compare the impact of different educational interventions on learning. In T. Barnes, M. Desmarais, C. Romero, & S. Ventura (Eds.), Proceedings of the 2nd International Conference on Educational Data Mining (EDM2009), 1–3 July 2009, Cordoba, Spain (pp. 51–60). International Educational Data Mining Society.

Ferguson, R., & Buckingham Shum, S. (2012). Social learning analytics: Five approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 23–33). New York: ACM.

Gibson, A., & Kitto, K. (2015). Analysing reflective text for learning analytics: An approach using anomaly recontextualisation. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 51–60). New York: ACM. doi:10.1145/2723576.2723635

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Grawemeyer, B., Mavrikis, M., Holmes, W., Guitierrez-Santos, S., Wiedmann, M., & Rummel, N. (2016). Affective off-task behaviour: How affect-aware feedback can improve student learning. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 104–113). New York: ACM.

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Hegazi, M. O., & Abugroon, M. A. (2016). The state of the art on educational data mining in higher education. International Journal of Computer Trends and Technology, 31, 46–56.

Howard, L., Johnson, J., & Neitzel, C. (2010). Examining learner control in a structured inquiry cycle using process mining. In R. S. J. d. Baker, A. Merceron, & P. I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (EDM2010), 11–13 June 2010, Pittsburgh, PA, USA (pp. 71–80). International Educational Data Mining Society.

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Lonn, S., Aguilar, S., & Teasley, S. D. (2013). Issues, challenges, and lessons learned when scaling up a learning analytics intervention. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK ’13), 8–12 April 2013, Leuven, Belgium (pp. 235–239). New York: ACM. doi:10.1145/2460296.2460343

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