## Data Mining for Instructional Design, Learning and Assessment

##### Author

Vicent Safont, Lluís

Gumara Rigol, Xavier

##### Other authors

Universitat Ramon Llull. La Salle

##### Publication date

2010-04##### DOI

http:doi.org/10.5772/8797

##### Abstract

Statistical analysis is widely used in many different areas: medicine, business, natural and social sciences, and of course, in education.
In this last topic, it is common that teachers make simple statistical analysis on the results of the students at the end of an exam or a course, and this is useful for the evaluation of that course. However a more powerful use of statistics can and must be done if the analyses are used to modify the methodology of learning personalizing contents and methods for groups
of students with similar skills. To make a realistic personalization of learning, data mining techniques must be used. They are also useful to manage big amounts of information mainly composed by: contents, skills, tools, grades and students.
In this chapter, we present data mining techniques used in instructional design, in learning and in the assessment of the students. In order to reduce, interpret and classify the information, factor and cluster analysis have been used.
Factor analysis is a technique that extracts few unobserved new variables (factors) from a big number of data. These factors are linear combinations of the observed variables and the expert analyzer must define the information that underlies each factor. Cluster analysis classifies all the information in some sets (clusters) of items with common features.
Let's present here two examples of the use of Data Mining in e-learning:
Example 1. An institution must decide its learning methodology, and it has planned to use a Learning Management System (LMS). Of course, an LMS contains many tools, and teachers and students must learn how to use these tools. But not
all these tools add value to learning, and probably many of them are redundant, that is, students can acquire the same competences using different tools. In (Vicent, 2007) teachers were asked to value (from 0 to 3) the performance of each tool (24 were considered) to develop each skill. Using factor and cluster analysis, an LMS of only 5 tools was defined to run an engineering online degree in the European Higher Education Area.
Example 2. If an LMS is used for learning, much information of the students is available: results of questionnaires, number of post in the forums, number of visits to the contents, etc. It is possible to classify the students in function of their
behavior with a cluster analysis. This way, lazy, willing, active, brillant, etc. students can be detected. Results must be used to modify the behavior of some students if needed.
It is obvious that students have different capacities to learn one topic or skill. And each student is better in some skills than in others. When the teachers create assignments or questionnaires, each of these assignments is assessing one or more
skills. Let's assume that at some point of a course, a teacher has collected 500 data of each student: questions answers, grades of assignments, forums posts, etc. Data mining techniques are definitively useful to interpret such amount of information. Factor analysis will simplify these 500 data in a few factors, each factor representing an unobserved variable with a real meaning that must be interpreted by the teacher. This factor will represent a skill or a set of skills. This technique
suppose an automatic tool to grade skills, even in the case that the teacher did not define, in the assignment or question, which skills were going to be developed and assessed. In addition, if a unique teacher manages a big group of students, they can be
classified in function of their performance in the skill/s of each factor. Cluster analysis will do this classification. This analysis makes the teacher able to write a good report on the state of learning of their students, giving several grades (one per
skill) to their students, and classifying the students in different groups taking into account their performance. If this analysis is done several times during a course, teachers can correct deficiencies in the achievement of some skills. In groups of
students, teachers can prepare an adaptive learning plan for each group. This adaptive learning plan should be a must for teachers whose students have to achieve a predefined set of skills. This method can be also applied to a global
degree, defining adaptive curricula for different groups of students.
In this chapter, the opportunities that the statistical analysis offer to teachers and managers of learning programs is presented.

##### Document Type

Chapter or part of a book

##### Language

English

##### Subject (CDU)

004 - Computer science and technology. Computing. Data processing

378 - Higher education. Universities. Academic study

##### Keywords

Mineria de dades

Ensenyament a distància

##### Pages

18 p.

##### Publisher

Intechopen

##### Is part of

E-learning Experiences and Future

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### Rights

© L'autor/a

Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/