. Five mechanisms to support the ongoing assessment the criterion used to decide whether or not the student
of distance learning were identified through this survey: has carried them out.
− tracking of the student’s actions;
− redirectioning through evaluation; 3. Ongoing Assessment of Distance Learning
− records of messages from lists; using Data Warehouse Resources
− records of messages from forums;
− records of messages from chats. The relevant information for ongoing assessment of
The results of this survey show a tendency for these distance learning can be stored in a data warehouse to
environments to support the tracking of some student support management decisions. This study explores the
activities to monitor his learning. use of a data warehouse with these characteristics for the
Most of these environments contain a small set of application of data mining techniques, allowing for
information that tracks the path the student has taken patterns of student behavior to be identified, thereby
during the course. This set varies from one favoring decision making for ongoing assessment of the
environment.to another, according to a criterion not student.
divulged by its designers. In this work, the modeling of the data warehouse
Although there is no standard set of requisites to assess follows the fact constellation schema , incorporating
the student’s learning, there are clearly two types of generalization hierarchies for fact or dimensions tables of
information to guide the implementation of ongoing the data warehouse.
assessment of learning in distance learning environments: Figure 1 constitutes part of the data warehouse that was
− Information about the student’s actions and developed based on the information discussed in the
communication . previous section. The gray boxes in these figures
This information can aid in understanding how the represent fact tables, i.e., tables that store information
student’s interactions with the environment and with about a subject, about which measures (or facts) are
other course participants influence his learning. Two defined (highlighted in bold). The remaining boxes
types of student interaction can be identified: represent the dimension tables from which one wishes to
− Student-Person Interactions: which are those in store the values that determine the fact table measures.
which the student interacts with other course The representation of a fact table with its dimension tables
participants, such as the teacher, the assistant is called Star Schema. Part A and B of Figure 1 represent
teacher or another student, through some two star schemas.
communication mechanism. With regard to these
interactions, it is interesting to know, for
instance, the subject of the message and the
mechanism (chat, email, list, forum, etc.)
− Student-Material Interactions: which are those in
which the student interacts with the didactic
material (content pages, tests, exercises, etc.).
About these interactions, it is interesting to
know, for example, how much time was spent on
them, if the interaction consisted of downloading
or uploading, which discipline the material
belongs to, what link was used to access the
− Information about the student’s activities in the
course  This kind of information, which depends
on a rule established by the teacher, strongly
influences in determining whether or not the student
has actually learned. Each activity proposed by the
teacher may have a result: for instance, participation
or not in a conference, the grade given for an
assignment, and so on. This type of information
depends on the activities proposed for the course and
Figure 1. Fact constellation schema for the Activity
the way the teacher has chosen to validate them, i.e.,
and Personal Interaction.
Information about the activities developed by the the measures and dimensions of these two facts can be
student during the course can be stored in the data analysed jointly, crossing information about the
warehouse, as illustrated in part A of Figure 1, while interactions and activities developed by the students. One
information about the student-person interactions can kind of analysis that can be made, for example, is to check
follow the model shown in part B of Figure 1. if the students’ interactions influence in the performance
The PersonalInteraction fact table shown in part B of of the course activities.
Figure 1 specializes in 4 different interactions: Figure 2 illustrates the fact constellation schema of the
InteractionViaChat, InteractionViaEmail, data warehouse developed to assess distance learning. A
InteractionViaList and InteractionViaForum. The fact constellation is a collection of stars.
semantics of this hierarchical structure is translated into In addition to the information about activities and
the measures and dimensions of the specialized facts. personal interactions, this data warehouse contains the
These fact tables contain all the dimensions and measures following information:
of the PersonalInteraction. In analytical terms, this − the student’s interaction (access) with the didactic
represents the possibility of examining, in each fact of the material (StudentMaterialInteraction fact table-
specialization, the .dimensions and measures common to centered), involving the attributes
all the personal interactions as well as the specific DurationOfTheAccess, LinkOfTheMaterialAccessed,
information about each interaction (via chat, via email, TypeOfAccess (download or upload), etc.
via list or via forum), considering the instances pertinent − the tests the student has taken (Test fact table-
to the fact table in question. For analytical purposes, the centered), with the attributes Grade,
PersonalInteraction fact table is used when one wishes to NumberOfIncorrectly AnsweredQuestions, etc.
analyze measures and attributes common to all the types − and whether the student has passed the tests upon
of personal interaction. conclusion of a discipline (Approval fact table-
An analysis of Figure 1 reveals that the stars of the centered), with the attributes Dropped-out?, Passed?,
Activity and PersonalInteraction facts have common TemporarilySuspended?, etc.
dimensions: Student, Course, Discipline, Institution, For purposes of legibility, Figure 2 groups the Student,
Group and Time. Joining these two stars forms a Course, Discipline, Institution, Time and Group
constellation with two facts that share six dimensions. dimensions shared by all the facts into one entity to avoid
This union is advantageous because, in addition to the pollution caused by linking.
avoiding the duplication of data, in practice it means that The data warehouse in Figure 2 shows various indirect
Figure 2. Fact constellation for ongoing assessement.
relationships among the fact tables. This opens up a wide Figures 3 and 4 exemplify the use of the MultiStar
range of possibilities when combining measures and environment for knowledge discovery in the data
dimensions to carry out analyses, e.g., warehouse in Figure 2. These figures portray how the
− analyze whether there is a relation between a selection and mining of information in this environment
student’s score, his personal interactions and his can be performed. Field 1 of Figure 3 represents the fact
accessing of the didactic material (involving the Test, tables of Figure 2 which, upon being expanded
PersonalInteraction and StudentMaterialInteraction (fields 2, 3 and 4), show the attributes that represent the
facts); subjects subjected to analysis in the fact table (called
− verify the influence of factors such as communication measures or facts) and information about the related
and study on learning (involving the dimension tables.
PersonalInteraction and StudentMaterialInteraction
− discover if the type of connection a student possesses
influences the number of times he accesses the
environment (involving the Student dimension and
the StudentMaterialInteraction fact);
− find activities that are more effective in given
courses, age groups, level of schooling, etc.
(involving the Course and Student dimensions and
the Activity fact).
These analyses can be made using the environment for
Knowledge Discovery in Data Warehouses (KDW)
described in the following section.
4. A KDW Application for Assessment of
Distance Learning Figure 3. MultiStar: selecting information.
Commercial tools can be used to carry out The purpose of the data selection process illustrated in
management analyses in the data warehouse presented in Figure 3 is to support an analysis of the influence of the
the previous section; however, they support simple chat interactions on the student’s activities. Thus, a
analyses, i.e., using only one fact and its dimension tables, selection was made in the data warehouse of the Student
e.g., identify the profiles of students more prone to dimension common to the Activity (field 2), Approval
dropping out of a course (involving the Student dimension (field 3) and PersonalInteraction (field 4) fact tables, the
table and the Approval fact table). TypeOfInteraction and Reply? measures in the
However, there are important analyses that can be PersonalInteraction fact table, the Passed? measure of
performed in this warehouse which require a comparison the Approval fact table, and the Accomplished? measure
of the different aspects of the student’s learning process. of the Activity fact table. This analysis was restricted to
Examples of this type of analysis were given in the students of the ATA Institution during the period of 1999
previous section. to 2001. This led to the creation of filters (field 5) for the
To support this type of broad analysis, i.e., those attribute Name of the dimension Institution (field 6) and
involving more than one fact (star), an environment called for the attribute Year of the dimension Time (field 7), both
MultiStar was developed for knowledge discovery . of which are attributes of dimensions common to the three
This environment allows information to be selected in fact tables.
which data mining tasks will be applied, providing The information selected is stored in a data cube2
resources for the recognition of fact constellations and the called ‘Interactions and Activities’, which contains all the
treatment of generalization hierarchies. By recognizing attributes of the Student dimension table (as shown in
.fact constellations, MultiStar allows for analyses Figure 1) and the measures cited below.
involving facts that belong to the same constellation, i.e., In the MultiStar environment, for a generalization
facts that share dimensions. The treatment of hierarchy between fact or dimension tables, characteristics
generalization hierarchies involving the relationship of inherited from the parent tables are displayed
inheritance among the fact or dimension tables of a data automatically in the child tables, making the hierarchies
warehouse does not require the user to understand the
concept on which it is based. A data cube  is a structure composed of dimensions and facts
organized to facilitate analyses of the data.
clear to the user. With regard to the fact constellations, The data mining task chosen was Classification, with
when a dimension or measure is selected, the MultiStar the purpose of classifying the student according to the
environment allows for the selection of only the fact measure Passed?.
tables that are related directly or indirectly with the When this mining task is performed, MultiStar
selected information. textually presents the patterns it finds. The patterns
resulting from the classification task are expressed
through rules, as shown in the example below:
IF Accomplished? = yes, and
TypeOfConnection = superfast, and
TypeOfInteraction = chat, and
Reply? = yes
THEN Passed? = yes
The number of cases in which a rule occurs and the
degree of reliability of the rule are indicated for each rule
This paper discusses the relevant information for
ongoing assessment of learning in computational distance
learning environments, proposing a solution to aid in
those ongoing assessment through the use of data
warehouse and data mining resources. Modeling of a data
warehouse was presented to illustrate the information
identified, as well as the MultiStar environment, which
allows for knowledge discovery in this data warehouse.
The authors intend to present the results of the
Figure 4. MultiStar: mining data. application of data mining tasks in the next version of the
environment in a more user intuitive form, using graphic
Once the data has been selected, MultiStar provides
resources for the application of data mining tasks so that
An intelligent tutor can also be developed to
patterns can be extracted based on those data. Figure 4
automatically guide the student in his learning process,
shows the interface for the application of data mining on
based on the results of the data mining tasks applied to the
the data selected in Figure 3.
data warehouse discussed herein.
In Field 1 of Figure 4, the user selects the cube to be
analyzed (the ‘Interactions and Activities’ cube was
selected here). Field 2 shows the attributes of the selected 6. References
cube (dimensions and measures). The user must choose
 W.H. Inmon, Building the Data Warehouse, John
one attribute from each dimension of the cube (the
Wiley & Sons, 2nd edition, 1996
attribute TypeOfConnection from the Student dimension
table was selected). These attributes together with the  R. Kimball, The Data Warehouse Toolkit – Practical
measures of the cube (Accomplished? from the Activity Techniques for Building Dimensional Data Warehouses,
.fact table, Passed? from the Approval fact table, and John Wiley Professio, 1996
TypeOfInteraction and Reply? from the
 R. Kimball, L. Reeves, M. Ross and W. Thornthwaite,
PersonalInteraction fact table, in our example) compose a
The Data Warehouse Lifecycle Toolkit, Willey Computer
view to be mined. Field 5 shows the cube filter selected.
A mining task is selected in Field 3, and the parameters
for this task are defined in Field 4. The data mining tasks  J. Han and M. Kamber, Data mining – Concepts and
available in the environment are Association , Techniques, 1 st edition, New York: Morgan Kaufmann,
Classification  and Clustering . Each of these 2000
tasks allows the data to be analyzed from a different
 K. Nurmela, E. Lehtinen, T. Palonen, Evaluating
CSCL Log Files by Social Network Analysis, In:
Computer Support for Collaborative Learning, Stanford,  D.R. Silva and M.T.P. Vieira, An Ongoing
USA, 1999. Proceedings. p. 434-441 Assessment Model in Distance Learning, In: Proceedings
of Internet and Multimedia Systems and Applications,
 M. Rahkila and M. Karjalainen, Evaluation of
Honolulu, USA, 2001
Learning in Computer Based Education Using Log
Systems. In: ASEE/IEEE Frontiers in Education  C. Vrasidas and M.S. McIsaac, Factors Influencing
Conference, 29., San Juan, Puerto Rico, 1999, Procedings. Interaction in an Online Course; The American Journal of
p. 16-21 Distance Education, v. 13, n. 3, 1999.
 S.L. Tanimoto, Towards an Ontology for Alternative  D.R. Silva, A Tool for Knowledge Discovery using
Assessment in Education. Metting of IEEE Learning Data Warehousing and its Application on the Ongoing
Technology Standards Committee, Pittsburgh, USA, 1998 Assessment of Distance Learning. MPhil. Dissertation,
Departament of Computer Science, UFSCar, São Carlos,
 J. Pei, J. Han, B. Mortazavi-Asl and H. Zhu, Mining
Brazil, 2002, 108p. (In portuguese)
Access Patterns Efficiently from Web Logs, In: Pacific-
Asia Conference on Knowledge Discovery and Data  R. Agrawal, T. Imielinski and A. Swami, Mining
Mining, Kyoto, Japan, 2000, Proceedings. p. 396-407 Associations between Sets of Items in Massive Databases.
In: ACM SIGMOD International Conference on the
 O.R. Zaiane, M. Xin and J. Han, Discovering Web
Management of Data. New York, USA, 1993.
Access Patterns and Trends by Applying OLAP and Data
Proceedings. NY: ACM Press, 1993, p. 207--216.
Mining Technology on Web Logs, In: Advances in Digital
Libraries Conference, Santa Barbara, USA, 1998,  J.R. Quinlan, Induction of Decision Trees. Machine
Proceedings. p. 19-29 Learning, 1:81-106, 1986
 B. Mortazavi-Asl, Discovering and Mining User  P. Cheeseman and J. Stutz, Bayesian Classification
Web-Page Traversal Patterns, MPhil. Dissertation, Simon (AutoClass): Theory and Results, In: Advances in
Fraser University, 1999, p. 93 Knowledge Discovery in Databases, 1995. 10.,
Proceedings. AAAI Press, p. 61-83, 1995