An automatic essay correction for an active learning environment

In this paper, we describe and test a part of an active learning environment that is being used to engage and empower students' learning in a undergraduate engineering course. We developed a survey system, called Milsa, in order to insert questions and template answers, and to automatically correct the questions based on template answers. In Milsa, the students insert multiple questions and template answers, the questions aleatory distributed to multiple responses. Milsa was used as an assessment in a gamified discipline [1]. After the automatic correction of the questions, the students evaluate the responses. The correction algorithm in Milsa is based on LSA [2], on STASIS [3] and on Michalski's genetic algoritm [4]. The template answer is a tagged text: hyperonymy, hyponymy, meronymy and synonymy are marked. By the end the tests on are showed and evaluated.

[1]  Zuhair Bandar,et al.  Sentence similarity based on semantic nets and corpus statistics , 2006, IEEE Transactions on Knowledge and Data Engineering.

[2]  J. McGonigal Reality Is Broken: Why Games Make Us Better and How They Can Change the World , 2011 .

[3]  Simon Coupland,et al.  A fast and efficient semantic short text similarity metric , 2013, 2013 13th UK Workshop on Computational Intelligence (UKCI).

[4]  Janaki Kumar,et al.  Gamification at Work: Designing Engaging Business Software , 2013, HCI.

[5]  Nadine Eberhardt,et al.  Computer Organization And Design 2nd Edition , 2016 .

[6]  Sérgio Antônio Andrade de Freitas,et al.  Using an Active Learning Environment to Increase Students' Engagement , 2016, 2016 IEEE 29th International Conference on Software Engineering Education and Training (CSEET).

[7]  Sérgio Antônio Andrade de Freitas,et al.  Smart Quizzes in the Engineering Education , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[8]  Yu-kai Chou,et al.  Actionable Gamification: Beyond Points, Badges, and Leaderboards , 2015 .

[9]  B. Bloom,et al.  Taxonomy of Educational Objectives. Handbook I: Cognitive Domain , 1966 .

[10]  Marc Prensky,et al.  Digital game-based learning , 2000, CIE.

[11]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[12]  David McLean,et al.  An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources , 2003, IEEE Trans. Knowl. Data Eng..

[13]  Tom Briggs Techniques for active learning in CS courses , 2005 .

[14]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[15]  Benjamin S. Bloom,et al.  A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives , 2000 .

[16]  Andrew S. Tanenbaum,et al.  Structured Computer Organization , 1976 .

[17]  Jan Dirk L. Fijnheer,et al.  Gamification , 2019, Encyclopedia of Education and Information Technologies.

[18]  William Stallings Computer Organization and Architecture , 2002 .

[19]  Petra Kaufmann The Gamification Of Learning And Instruction Fieldbook Ideas Into Practice , 2016 .

[20]  Danilo Sipoli Sanches,et al.  Comparative Study of Genetic Algorithm and Ant Colony Optimization Algorithm Performances for the Task of Guitar Tablature Transcription , 2015, 2015 Brazilian Conference on Intelligent Systems (BRACIS).