@article{Khaneboubi2023,
  author    = {Khaneboubi, Mehdi},
  title     = {Visualizing students flows to monitor persistence},
  series = {EMOOCs 2023 : Post-Covid Prospects for Massive Open Online Courses - Boost or Backlash?},
  journal   = {EMOOCs 2023 : Post-Covid Prospects for Massive Open Online Courses - Boost or Backlash?},
  editor    = {Meinel, Christoph and Schweiger, Stefanie and Staubitz, Thomas and Conrad, Robert and Alario Hoyos, Carlos and Ebner, Martin and Sancassani, Susanna and Żur, Agnieszka and Friedl, Christian and Halawa, Sherif and Gamage, Dilrukshi and Scott, Jeffrey and Kristine Jonson Carlon, May and Deville, Yves and Gaebel, Michael and Delgado Kloos, Carlos and von Schmieden, Karen},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  doi       = {10.25932/publishup-62390},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-623906},
  pages     = {121 -- 131},
  year      = {2023},
  abstract  = {Founded in 2013, OpenClassrooms is a French online learning company that offers both paid courses and free MOOCs on a wide range of topics, including computer science and education. In 2021, in partnership with the EDA research unit, OpenClassrooms shared a database to solve the problem of how to increase persistence in their paid courses, which consist of a series of MOOCs and human mentoring. Our statistical analysis aims to identify reasons for dropouts that are due to the course design rather than demographic predictors or external factors.We aim to identify at-risk students, i.e. those who are on the verge of dropping out at a specific moment. To achieve this, we use learning analytics to characterize student behavior. We conducted data analysis on a sample of data related to the "Web Designers" and "Instructional Design" courses. By visualizing the student flow and constructing speed and acceleration predictors, we can identify which parts of the course need to be calibrated and when particular attention should be paid to these at-risk students.},
  language  = {en}
}
@article{WasilewskiKhaneboubiBruillard2023,
  author    = {Wasilewski, Julie and Khaneboubi, Mehdi and Bruillard, {\´E}ric},
  title     = {How to detect At-Risk learners in professional finance MOOCs},
  series = {EMOOCs 2023 : Post-Covid Prospects for Massive Open Online Courses - Boost or Backlash?},
  journal   = {EMOOCs 2023 : Post-Covid Prospects for Massive Open Online Courses - Boost or Backlash?},
  editor    = {Meinel, Christoph and Schweiger, Stefanie and Staubitz, Thomas and Conrad, Robert and Alario Hoyos, Carlos and Ebner, Martin and Sancassani, Susanna and Żur, Agnieszka and Friedl, Christian and Halawa, Sherif and Gamage, Dilrukshi and Scott, Jeffrey and Kristine Jonson Carlon, May and Deville, Yves and Gaebel, Michael and Delgado Kloos, Carlos and von Schmieden, Karen},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  doi       = {10.25932/publishup-62481},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-624818},
  pages     = {305 -- 316},
  year      = {2023},
  abstract  = {"Financial Analysis" is an online course designed for professionals consisting of three MOOCs, offering a professionally and institutionally recognized certificate in finance. The course is open but not free of charge and attracts mostly professionals from the banking industry. The primary objective of this study is to identify indicators that can predict learners at high risk of failure. To achieve this, we analyzed data from a previous course that had 875 enrolled learners and involve in the course during Fall 2021. We utilized correspondence analysis to examine demographic and behavioral variables. The initial results indicate that demographic factors have a minor impact on the risk of failure in comparison to learners' behaviors on the course platform. Two primary profiles were identified: (1) successful learners who utilized all the documents offered and spent between one to two hours per week, and (2) unsuccessful learners who used less than half of the proposed documents and spent less than one hour per week. Between these groups, at-risk students were identified as those who used more than half of the proposed documents and spent more than two hours per week. The goal is to identify those in group 1 who may be at risk of failing and those in group 2 who may succeed in the current MOOC, and to implement strategies to assist all learners in achieving success.},
  language  = {en}
}