A system for formative assessment and monitoring of students' progress

Abstract Assessment plays a central role in any educational process as a way of evaluating the students' knowledge on the concepts associated with learning objectives. The assessment of free-text answers is a process that, besides being very costly in terms of time spent by teachers, may lead to inequities due to the difficulty in applying the same evaluation criteria to all answers. This paper describes a system composed by several modules whose main goal is to work as a formative assessment tool for students and to help teachers creating and assessing exams as well monitoring students' progress. The system automatically creates training exams for students to practice based on questions from previous exams and assists teachers in the creation of evaluation exams with various kinds of information about students' performance. The system automatically assesses training exams to give automatic feedback to students. The correction of free-text answers is based on the syntactic and semantic similarity between the student answers and various reference answers, thus going beyond the simple lexical matching. For this, several pre-processing tasks are performed in order to reduce each answer to its more manageable canonical form. Besides the syntactic and semantic similarity between answers, the way the teacher evaluates the answers is also acquired. To accomplish that, the assessment is done using sub scores defined by the teacher concerning parts of the answer or its subgoals. The system has been trained and tested on exams manually graded by History teachers. There is a good correlation between the evaluation of the instructors and the evaluation performed by our system.

[1]  Fakhroddin Noorbehbahani,et al.  The automatic assessment of free text answers using a modified BLEU algorithm , 2011, Comput. Educ..

[2]  Marian Petre,et al.  E-Assessment using Latent Semantic Analysis in the Computer Science Domain: A Pilot Study , 2004 .

[3]  Claudia Leacock,et al.  Automated evaluation of essays and short answers , 2001 .

[4]  Sebastián Ventura,et al.  Classification via clustering for predicting final marks starting from the student participation in Forums , 2012, EDM.

[5]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[6]  David Callear,et al.  Bridging gaps in computerised assessment of texts , 2001, Proceedings IEEE International Conference on Advanced Learning Technologies.

[7]  Ismael Pascual-Nieto,et al.  Computer-assisted assessment of free-text answers , 2009, The Knowledge Engineering Review.

[8]  D. Whittington,et al.  Approaches to the computerized assessment of free text responses , 1999 .

[9]  Leah S. Larkey,et al.  Automatic essay grading using text categorization techniques , 1998, SIGIR '98.

[10]  Benjamin S. Bloom,et al.  Taxonomy of Educational Objectives: The Classification of Educational Goals. , 1957 .

[11]  Lawrence M. Rudner,et al.  Automated Essay Scoring Using Bayes' Theorem , 2002 .

[12]  Hao Xu,et al.  Assisting Instructor Assessment of Undergraduate Collaborative Wiki and SVN Activities , 2012, EDM.

[13]  Graeme Hirst,et al.  Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures , 2004 .

[14]  O. Mason,et al.  Automated free text marking with Paperless School , 2002 .

[15]  Mohammad Al-Smadi,et al.  SOA-based architecture for a generic and flexible e-assessment system , 2010, IEEE EDUCON 2010 Conference.

[16]  Fátima Rodrigues,et al.  Automatic Assessment of Short Free Text Answers , 2012, CSEDU.

[17]  Janice D. Gobert,et al.  Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds , 2012, EDM 2012.

[18]  Rui P. Chaves,et al.  WordNet.PT New Directions , 2006 .

[19]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[20]  Alejandro Peña-Ayala Review: Educational data mining: A survey and a data mining-based analysis of recent works , 2014 .

[21]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[22]  Sanna Järvelä,et al.  Patterns in elementary school students′ strategic actions in varying learning situations , 2013 .

[23]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[24]  E. B. Page Computer Grading of Student Prose, Using Modern Concepts and Software , 1994 .

[25]  Tom Mitchell,et al.  Towards robust computerised marking of free-text responses , 2002 .