=Paper=
{{Paper
|id=Vol-1446/GEDM_2015_Submission_5
|storemode=property
|title=Studio: Ontology-Based Educational Self-Assessment
|pdfUrl=https://ceur-ws.org/Vol-1446/GEDM_2015_Submission_5.pdf
|volume=Vol-1446
|dblpUrl=https://dblp.org/rec/conf/edm/WeberV15
}}
==Studio: Ontology-Based Educational Self-Assessment==
https://ceur-ws.org/Vol-1446/GEDM_2015_Submission_5.pdf
Studio: Ontology-Based Educational Self-Assessment
Christian Weber Réka Vas
Corvinno Technology Corvinus University
Transfer Center of Budapest
Budapest, Hungary Budapest, Hungary
cweber@corvinno.com reka.vas@uni-corvinus.hu
ABSTRACT cognitive behavior. In his discussion, eight out of nine knowledge
Students, through all stages of education, grasp new knowledge types underline that knowledge in the scope of learning is
in the context of knowledge memorized all through their previous interrelated and strongly associated with previous experiences
education. To self-predict personal proficiency in education, self- [2]. As such, a supporting solution for self-assessment should
assessment acts as an important learning feedback. The in-house grasp and formalize the knowledge to assess in the context of
developed Studio suit for educational self-assessment enables to related knowledge.
model the educational domain as an ontology-based knowledge The Studio suit for educational self-assessment, presented in this
structure, connecting assessment questions and learning material paper, provides here a software solution for testing the personal
to each element in the ontology. Self-assessment tests are then proficiency in the context of related knowledge. It enables to
created by utilizing a sub-ontology, which frames a tailored model areas of education as a substantial source for assessment
testing environment fitting to the targeted educational field. In and narrows the gap between a potentially flawed self-prediction
this paper we give an overview of how the educational data is and the real proficiency, by offering an objective and adaptive
modeled as a domain ontology and present the concepts of online knowledge-test. To follow the natural learning process
different relations used in the Studio system. We will deduct how and enable an easy extension, the software embeds the assessed
the presented self-assessment makes use of the knowledge knowledge into a network of contextual knowledge, which
structure for online testing and how it adapts the test to the enables to adapt the assessment to the responses of the students.
performance of the student. Further we highlight where
potentials are for the next stages of development. This paper will give an overview of the Studio educational
domain ontology and the aspects of the system supporting
Keywords personalized self-assessment. Further it will highlight potentials
for data mining on the gathered educational data with an outlook
Education, adaptive test, self-assessment, educational ontology on the next stages of evaluation.
1. INTRODUCTION 2. THE STUDIO APPROACH FOR SELF-
Students exploring new fields of education are always confronted ASSESSMENT
with questions regarding their individual progress: how much do The basic concept of Studio is to model the focused education as
they know after iterations of learning, in which directions should an interrelated knowledge structure, which divides the education
they progress to fill the field most effectively, how to grasp the into sub-areas and knowledge items to know. The managed
outline and details of the field and how much of their time structure formalizes the relation between knowledge areas as a
should they invest in learning? Especially in higher education, learning context and models the requirements to master specific
where learning becomes a self-moderated, personalized process, parts of the education. This structure is used to create and
students are in need of continuous self-assessment to capture support knowledge tests for students. Through this combination
their current state of proficiency. At the same time, the of assessment and knowledge structure, the student gains the
unframed, informal self-prediction of students regarding their freedom to explore not only single knowledge items but the
personal skills is often substantive and systematically flawed [1]. education in the context of related knowledge areas, while the
Here a systematic and objective solution for self-assessment is embedded requirements are used to map the modeled knowledge
substantial to prevent a wrong or biased self-evaluation and to against the expected educational outcome.
support the self-prediction of the personal proficiency.
The assessment-system is designed to be accompanied by phases
Following Jonassen, knowledge in education could be split into of learning within the system, where the student gets access to
nine types across three categories to capture the human’s learning material, based on and supported by the test feedback.
This combined approach offers a unique self-assessment to the
students, where the backing knowledge context is used to adapt
the assessment in dependency of the test performance of the
student.
Before any regular examination students may use Studio to
assess their knowledge on their own. It is the tutor’s
responsibility to set the course of self-assessment test in Studio
system by selecting knowledge areas and sub-knowledge areas aspects of education as the curriculum or aspects relevant for the
which are relevant for the target education from the domain task of learning and course creation [8][9][10] or describe the
ontology. Then the frame will be automatically completed with design, use and retrieval of learning materials till creating
elements from the ontology which detail the selected knowledge courses [11], as well as directly the learner within the education
areas and are modeled as required for this part of the education. [12].
As the system stores assessment questions for each knowledge Within the area of educational ontologies, domain ontologies
element, Studio will then automatically prepare an assessment tend to model too specific details of the education, in an attempt
test, based on the defined selection and the domain ontology. The to model the specific field as complete as possible. This enables
resulting knowledge-test is then accessible as a self-assessment a comprehensive view on the field but it comes at the cost of
test for the student, who explores the backed knowledge generality, with the potential to be inflexible to handle changes.
structure, which pictures the expected learning outcome, in Other concepts model the education across different ontologies,
cycles of testing, reflection and learning. The process of test matching concepts like the learner, the education and the course
definition and assessment is shown in Figure 1, while the result description, introducing a broad horizon but with additional
preparation for reflection and learning is discussed in section 2.5. overhead to combine modelled insights and reason on new
instances.
The appeal of the Studio educational ontology is the size and
focus of the main classes and their relationships between each
other. The knowledge to learn is the main connecting concept in
the core of education. It enables a great flexibility to be
resourceful for different education related questions. An example
is here the business process management extension PROKEX,
which maps process requirements against knowledge areas to
create assessment test, reflecting the requirements of attached
processes [13].
An important factor in learning is the distance between the
expectation of the tutor and the learning performance of the
student. Here a short cycle of repeated assessment and learning
is a major factor for a better personal learning performance [14].
This aspect directly benefits from the focused concentration on
knowledge-areas as the main exchange concept between students
and tutors. As even further the close connections between
learners and educators via direct tutoring is one major enabler for
computer aided systems [15], each step towards a more direct
interaction through focused concepts is an additional supporter.
The class structure fuses the idea of interrelated knowledge with
a model of the basic types of educational concepts, involved in
situations of individual learning. Figure 2 visualizes the class
concepts as knowledge elements, together with the relation types,
used to model the dependencies between different aspects of
knowledge and learning within the educational ontology.
The Knowledge Area is the super-class and core-concept of the
ontology. The ontology defines two qualities of main relations
between knowledge areas: Knowledge areas could be a sub-
knowledge area of other knowledge areas with the “has_sub-
Figure 1: The overall design, assess and reflection cyle of the knowledge_area” relation or be required for another knowledge
system. area with the “requires_knowledge_of” relation. A knowledge
area may have multiple connected knowledge areas, linked as a
2.1 The Educational Domain Ontology requirement or sub-area. The “requires_knowledge_of” relation
The Studio system is based on a predesigned educational
defines that a node is required to complete the knowledge of a
ontology, explained in detail by Vas in [3]. Domain ontology is a parent knowledge area. This strict concept models a requirement
frequently used term in the field of semantic technologies and
dependency between fields of knowledge in education and yields
underlines the storage and conceptualization of domain
the potential to assess perquisites of learning, analog to the basic
knowledge and is often used in a number of projects and
idea of perquisites within knowledge spaces, developed by
solutions [4][5][6] and could address a variety of domains with Falmagne [16].
different characteristics in their creation, structure and
granularity, depending on the aim and the modeling person [7]. A Education is a structured process which splits the knowledge to
specialization in terms of the field is the educational domain learn into different sub-aspects of learning. Knowledge areas in
ontology which is a domain ontology adapted to the area and the ontology are extended by an additional sub-layer of
concepts of education. They could target to model different knowledge elements in order to effectively support educational
and testing requirements. Figure 2 visualizes the sub-elements 2.3 Creating and Maintaining Tests
and their relations. By splitting the assessed knowledge into sub- The creation and continues maintenance of the domain ontology
concepts, the coherence and correlation of self-assessment is a task of ontology engineering. The ontology engineer (the
questions could be expressed more efficiently and with the ontologist), creates, uses and evaluates the ontology [17], with a
potential of a more detailed educational feedback. strong focus on maintaining the structure and content. Within
Studio, this process is guided and supported by a specialized
Has sub- Requires administration workflow and splits in three consecutive task
Knowledge Area areas, in line with decreasing access rights:
knowledge area knowledge of
Ontology engineering (instance level): The creation
and linking of instances of the existing knowledge-area
Part of Part of classes into the overall domain ontology.
Part of
Test definition: Knowledge areas, which are relevant
to a target self-assessment test, are selected and
Refers grouped into specialized containers called Concept
Basic Concept Example
to
Refers to Groups (CG). These concept groups are organized into
a tree of groups, in line with the target of the
assessment. The final tree in this regards captures a
Refers to
sub-ontology. Concept groups are internally organized
based on the overall ontology and include all relations
Pr
Co
em
nc
between knowledge elements, as defined within the
ise
lu
s
domain ontology.
io
n
Theorem Refers
to Question and learning material creation: Questions
and learning materials alike are directly connected to
single knowledge areas within the designed test frame
and get imported, if already existing, from the domain
Test Learning ontology. More questions and learning materials are
Questions Material defined now, in line with the additional need of the
Testbank
targeted education and are available for future tests.
The pre-developed structure of classes and relations is fixed as
Figure 2: Model of the educational ontology. the central and integral design of the system. A view of the
Theorems express in a condensed and structured way the system interface for administration is provided in Figure 3. The
fundamental insights within knowledge areas. They fuse and left area shows the visualization of the current ontology section
explain the basic concepts of the depicted knowledge and set in revision and the right area shows the question overview with
them in relation to the environment of learning with examples. editing options. Tabs give access to additional editing views,
Multiple theorems could be “part_of” a knowledge area. Each including the learning material management and interfaces to
theorem may define multiple Basic Concepts as a “premise” or modify relations between nodes and node descriptions.
“conclusion”, to structure how the parts of the knowledge area
are related. Examples enhance this parts as a strong anchor for 2.4 Adaptive Self-Assessment
self-assessment questions and “refer_to” the theorems and basic To prepare an online self-assessment test, the system has to load
concepts as a “part_of” one or more knowledge areas. the relevant educational areas from the domain ontology and
extract the questions and relations of the filtered knowledge
2.2 The Testbank areas.
In order to connect the task of self-assessment with the model of The internal test algorithm makes use of two assumptions:
the educational domain, the system integrates a repository of
assessment questions. Each question addresses one element of Knowledge-area ordering: As the main knowledge
the overall knowledge and is directly associated with one areas are connected through “requires_knowledge_of”
knowledge area or knowledge element instance within the and “part_of” relations, every path, starting with the
ontology. The domain ontology provides here the structure for the start-element, will develop on average from general
online self-assessment while the repository of questions concepts to detailed concepts - given that the concept
supplements the areas as a test bank. The target of the self- groups in the test definition are also selected and
assessment is to continuously improve the personal knowledge ordered to lead from general to more detailed groups.
within the assessed educational areas, by providing feedback on Knowledge evaluation dependency: If a person,
the performance after each phase of testing. To do so, the Studio taking the test, fails on general concepts he or she will
system includes Learning Material connected to the test bank and potentially also fail on more detailed concepts. Further,
the knowledge areas, analog to the test questions. The learning if a high number of detailed concepts are failed, the
material is organized into sections as a structured text with parent knowledge isn’t sufficiently covered and will be
mixed media, as pictures and videos, and is based on a wiki- derived as failed, too.
engine to maintain the content, including external links.
Figure 3: The main ontology maintenance and administration interface, showing a part of the domain ontology.
The filtering is done based on the selection of a tutor, acting as same level. If the learner’s answer is correct, the system will
an expert for the target educational area. The tutor chooses activate the child elements of the current node and draw a
related areas, which are then created as a Test Definition, random question from the first left child.
containing Concept Groups, as described in section 2.3. The Based on the tree shaped knowledge structure, the assessment
system then uses the test definition as a filtering list to extract now follows these steps to run the self-assessment, supported by
knowledge areas. After the extraction, the structure is cached as the extracted knowledge structure:
a directed graph, while the top element of the initial concept
group is set as a start element. Beginning with the start-element, 1. Starting from the start-element, the test algorithm will
the test will move then through the graph, while administering activate the child knowledge-areas of the start element.
the questions connected to knowledge areas and knowledge 2. The algorithm now selects the first child-knowledge
elements. area and draws a random question out of the pool of
The loading of knowledge-elements follows three steps: available questions for this specific knowledge-element
from the test bank.
1. Each type of relation between two knowledge-elements
implements a direction for the connection. Assuming 3. If the learner fails the question, the algorithm will
the system loads all relations, starting with the start- mark the element as failed and select the next
element and ending on a knowledge-element, this knowledge area from the same level. If the learner’s
creates a two level structure where the start-node is a answer is correct, the system will activate the child
parent-element and all related, loaded elements are elements of the current node and trigger the process for
child-elements, as seen below in Figure 4. each child-element.
2. The loading algorithm then selects one child-element
and assumes it as a start-element and repeat the
loading process of knowledge-elements.
3. When no knowledge-elements for a parent-element
could be loaded, the sub-process stops. When all sub-
processes have stopped, the knowledge structure is
fully covered.
The test algorithm will now activate the child knowledge areas of
the start element and select the first knowledge area to the left
and draw a random question from the selected knowledge area. If Figure 4: Excerpt from the sub-ontology visualization, with
the learner fails the question, the algorithm will mark the the visible parent-child relationship, as used in the data-
element as failed and selects the next knowledge area from the loading for preparing the self-assessment.
An example question is shown below in Figure 5. Further JavaScript framework [18]. The visualization itself is a custom
following the testing algorithm, the system dives down within the build, similar to the Ext JS graph function “Radar” and based on
domain ontology and triggers questions depending on the the idea of Ka-Ping, Fisher, Dhamija and Hearst [19]. All views
learner’s answers and the extracted model of the relevant are able to zoom in and out of the graph, move the current
education. In this regards the Studio system adapts the test on the excerpt and offer a color code legend, explaining the meaning of
fly to the performance of the learner. Correlating to the idea of the colored nodes. In comparison with state of the art, the
adaptation, the learner will later gain access to learning material interface offers no special grouping or additional visualization
for each mastered knowledge area. As the learner continues to features like coding information into the size of nodes. Each
use the self assessment to evaluate the personal knowledge, he or interface offers an additional textual tree view to explore the
she will thus explore different areas of the target education, knowledge-elements or concept groups in a hierarchical listing.
following their individual pace of learning. This simple, straightforward approach for visualization correlates
with the goal of a direct and easy to grasp feedback through
interfaces which have a flat learning curve and enable to catch
the functionality in a small amount of time.
While this simple visualization is sufficient for the reasonable
amount of knowledge-elments within the result view, this alone
is not suitable for the domain ontology administration interface,
Figure 5: Test interface with a random drawn test question. as seen in Figure 3. Here Studio realizes methodologies to filter
and transform the data to visualize. To do so it makes use of two
2.5 Test Feedback and Result Visualization supporting mechanisms:
An important aspect of the system is the test feedback and The maximum-level-selector defines the maximum
evaluation interface. The educational feedback is one of the main level the system extracts from the domain ontology for
enabler for the student to grasp the current state and extend of full screen visualization.
the personal education. The domain ontology models the
structure and the dependencies of the educational domain, and In combination with the maximum level, the ontologist
the grouped test definition extracts the relevant knowledge for could select single elements within the domain
the target area or education. As such, the visualization of the ontology. This triggers an on-demand re-extraction of
ontology structure extracted for the test, together with the the visualized data, setting the selected knowledge-
indication of correct and incorrect answers, represents a map of element as the centre element. The system then loads
the knowledge of the learner. the connected nodes, based on their relations into the
orientation circles till the maximum defined level is
Throughout each view onto the ontology, the system uses the reached. More details about the transformation are in
same basic visualization, making use of the Sencha Ext JS [19].
Figure 6: Result visualization as educational feedback for the learner.
Together, this selection and transformation mechanism enables Each event stores information about the system in 7
the fluent navigation within the complete domain ontology dimensions, as described in Table 1 below:
structure, while re-using the same visualization interface. Table 1: Event blueprint to store events concerning system
Figure 6 shows the main view of the result interface. The left interaction.
area shows the sub-ontology extracted for the test, while the
Attribute Description
colored nodes represent the answers to the administered
questions. A red node visualizes wrong answers, while orange Event description code Which type of event and what
nodes are rejected nodes with correct answers but with an factors are relevant.
insufficient number of correctly answered child nodes, Location code On which part of the assessment-
indicating a lack of the underlying knowledge. Green nodes process or interface the event has
represent accepted nodes with correct answers and a sufficient occurred.
amount of correctly answered questions for child nodes. Grey
nodes are not administered nodes, which were not yet reached Session identifier Each access of the system is one
by the learner, as higher order nodes had no adequate session for one user.
acceptance. Numerical value storage Multi-purpose field, filled
Even though the target of the system is not a strict evaluation in depending on the event type.
number, the evaluation of the percentage of solved and String value storage Multi-purpose field, filled
accepted knowledge elements helps the learner to track the depending on the event type.
personal progress and could additionally be saved as a report
for further consultation. Besides providing an overview of the Event-time The time of the start of the
event.
self-assessment result, the result interface gives access to the
integrated learning material. For every passed node, the learner Item reference A unique reference code,
can now open the correlated material and intensify the identifying the correlated item
knowledge for successful tested areas. within the ontology. E.g. a
Retaking the test in cycles of testing and learning, while question or a knowledge-element
adapting the educational interaction, is the central concept of ID.
the Studio approach for self-assessment. As a consequence the All events are stored in order of their occurrence, so if no
system will not disclose the right answers to questions or explicit end event is defined, the next event for the same
learning material for not yet administered knowledge areas, to session and user is acting as the implicit end date. Extending
promote an individual reflection on the educational content the existing storage of information within Studio, the new
outside of a flat memorization of content. logging system stores the additional events, as shown in Table
2 below:
3. SYSTEM EVALUATION
Table 2: Assessment events and descriptions.
The system has been used, extended and evaluated in a number
of European and nationally funded research projects, including Event type Description
applications in business process management and innovation- START_TEST Marks the start of a test.
transfer [20], medical education [21] and job market
competency matching [22]. END_TEST Marks the end of a test.
Currently the system is being evaluated based on a running OPEN_WELCOME_LM The user opened the welcome
study with 200 university students in the field of business page.
informatics. The study will conclude on two current research OPEN_LM_BLOCK The student opened a learning
streams which are improving the systems testing and analysis material block on the test
capability. The first direction looks into potentials for the interface.
integration of learning styles into adaptive learning systems to
offer valuable advice and instructions to teachers and students OPEN_LM The student opened the learning
[23]. Within the second direction the question is challenged on material tab on the test interface.
how to adapt the presented self-assessment further towards the RATE_LM The student rated the learning
performance of the students, based on extracting assessment material.
paths from the knowledge structure [24].
CHECK_RESULT The student opened a result page.
For each running test, Studio collects basic quantitative data
about the number of assigned questions, how often tests are CONTINUE_TEST The student submitted an answer.
taken and how many students open which test and when. This FINISH_TEST The test has been finished.
is completed by qualitative measures, collecting which
SUSPEND_TEST The user suspended the test.
questions and knowledge elements the students passed or
failed. To conclude further on the mechanisms and impacts of RESUME_TEST The user has restarted a previously
Studio within the current study, a new logging system was suspended test.
developed, collecting the interaction with the system and SELECT_TEST_ALGO- The algorithm used to actually test
detailed information about the feedback as detailed events. RITHM the student is selected.
TEST_ALGORITHM_- The behavior of the current test text,” Expert Systems with Applications, vol. 34, no. 2, pp.
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