=Paper=
{{Paper
|id=Vol-3659/IJCKG_2023_WS1
|storemode=property
|title=Knowledge Modeling of the Inquiry based Learning Instructional Process in Japanese High Schools - Ontology model of Instruction Based on the PPDAC Cycle
|pdfUrl=https://ceur-ws.org/Vol-3659/IJCKG_2023_WS1.pdf
|volume=Vol-3659
|authors=Hiroki Hayashi,Munehiko Sasajima
|dblpUrl=https://dblp.org/rec/conf/jist/HayashiS23
}}
==Knowledge Modeling of the Inquiry based Learning Instructional Process in Japanese High Schools - Ontology model of Instruction Based on the PPDAC Cycle==
https://ceur-ws.org/Vol-3659/IJCKG_2023_WS1.pdf
Knowledge Modeling of the Inquiry-based Learning
Instructional Process in Japanese High Schools
Ontology model of Instruction Based on the PPDAC Cycle
Hiroki Hayashi1,∗,† , Munehiko Sasajima1,†
1
University of Hyogo, Nishimachi 8-2-1, Kobe, Hyogo, Japan, 651-2197
Abstract
The development of problem-solving skills has gained importance worldwide. Japanese high schools are
aiming to develop problem-solving skills through inquiry-based learning to discover and solve problems.
However, there is a lack of teachers capable of teaching inquiry-based learning, and instructional methods
for inquiry-based learning have not been established. To increase the number of teachers who can
teach inquiry-based learning, a systematic teaching method must be established. This study proposed
the Problem-Plan-Data-Analysis-Conclusion cycle (PPDAC cycle) ontology as a knowledge model that
facilitates the standardization of instructional methods in inquiry-based learning. First, knowledge
regarding the states of understanding in inquiry-based learning were extracted and systematized. Next,
Consequently, a knowledge model was constructed by structuring the systematized knowledge based
on the PPDAC cycle. The constructed ontology validated through a survey of high school teachers and
university professors. The results indicated that the proposed ontology could support inexperienced
teachers in planning instruction for inquiry-based learning using the PPDAC cycle. The proposed
ontology is a heavyweight one, and each concept definition has rich properties, is expected to help
computers infer instructional strategies for inquiry-based learning in high schools and to build a system
that outputs instructional plans according to students’ abilities and learning environments.
Keywords
High school Education, Inquiry-based learning, PPDAC Cycle, Ontology, Knowledge Modeling
1. Introduction
The development of technology has ushered in the sophistication of abilities required of human
beings. As the OECD international survey PISA 2012[1] measures problem-solving skills, the
cultivation of problem-finding and problem-solving skills has gained importance 21st century.
In response to these trends, inquiry-based learning along the problem-solving process has been
popular in Japanese high school education in order to cultivate problem-solving skills[2].
The Japanese high school education curriculum contains a course referred to as ”Period for
Inquiry-Based Cross-Disciplinary Study” which is designed for students to conduct inquiry-
based learning. In addition, other subjects have been shown to contain inquiry-based learning.
KGR4XAI2023: The 2nd International Workshop on Knowledge Graph Reasonig for Explainable Artificial Intelligence,
December 09, 2023, Tokyo, Japan
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open happyman122055@gmail.com (H. Hayashi); sasajima@sis.u-hyogo.ac.jp (M. Sasajima)
© 2023 Copyright © 2023 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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This indicates that Japanese high school education actively promotes the development of
problem-solving skills.
However, there are still certain problems yet to be solved. There are no government approved
textbooks for the ”Period for Inquiry-Based Cross-Disciplinary Study” and there is no standard
instructional content and methodology. Furthermore, there are no specific teachers specializing
in teaching students, and teachers are assigned independently from among teachers of various
subjects.
In addition, in Japan, the Ministry of Education, Culture, Sports, Science and Technology
(MEXT) has guidelines for school education in each subject. In the field of inquiry-based learning
for each subject in the curriculum guidelines, the problem-solving process was exemplified.
In the field of inquiry-based cross-disciplinary study, the process of inquiry is presented as
follows[3]: ”setting an issue, collecting information, organizing and analyzing, and summarizing
and expressing.” In the field of Mathematics I, the statistical inquiry process is presented as
follows[4]: ”problem - plan - data - analysis - conclusion” In the field of Informatics I, the
following practical training process is presented as follows[5]: ”Discover a problem, Collect
information and Define the problem, Searching for solutions and Developing a plan, Predicting
Results and Execution of the plan, reflection, and then solve the next problem.”
People with experience related to the problem-solving process and who have written papers in
university can understand that these three are practically the same process. However, it is doubt-
ful that high school teachers can judge the three processes to be the same. Prior research[6][7]
has indicated that the ability to teach inquiry-based learning is difficult without master level
academic experience. Currently, 84.1 % of teachers may not have academic experiences to
facilitate the discovery and solution of problems[8]. To teach inquiry-based learning, certain
experience in this regard along with academic experience is necessary. Therefore, although
inquiry-based learning are becoming increasingly important for developing problem-solving
skills, teachers capable of teaching inquiry-based learning in high schools are limited.
Many high school teachers do not understand how to teach inquiry-based learning owing
to their lack of academic experience. Therefore, they have encounter difficulties in planning
instruction for inquiry-based learning. For example, the stage of ”discovering a problem”
comprises six states: discovering a problem, formulating a question, setting a hypothesis,
setting an objective variable from the hypothesis, searching for an explanatory variable that
affects the objective variable, and determining an explanatory variable that leads to a solution.
When the state of determining the explanatory variables that lead to a solution is reached, a
researchable problem is considered to have been discovered. For inexperienced teachers to
plan lessons, it is necessary to define the process of inquiry-based learning in further detail as
presented in the Courses of Study. In addition, the process must be mapped to the content of
the lessons that the students are to be taught, such that it can be put into practice.
This study aimed at the standardization of the teaching method for inquiry-based learning
using the problem-solving process. First, we extracted and systematized the knowledge about
the problem-solving process and then the procedural knowledge was structured.
For this purpose, an ontology engineering tool suitable for making implicit information
explicit, systematizing knowledge, and standardizing knowledge in a certain domain was
used. The proposed ontology is a heavyweight one, and each concept definition has rich
properties. By using the ontology, we plan to support inexperienced teachers by inferring
instructional strategies for inquiry-based learning in high schools and to build a system that
outputs instructional plans according to the teachers’ abilities, students’ abilities and their
learning environments.
The results of this study resulted in the creation of a guide for teaching inquiry-based learning
and is expected to contribute to the development of teachers who can teach inquiry-based
learning.
This paper is organized as follows. Chapter 2 describes related research on ontology engi-
neering. In Chapter 3, we model the process of inquiry learning based on the PPDAC cycle, and
in Chapter 4, we construct the ontology corresponding to the model. In Chapter 5, we discuss
the evaluations of the constructed ontology received from high school and university teachers
who are experts in inquiry based learning, and in Chapter 6, we summarize and discuss future
prospects.
2. Related Works on Ontology Engineering
2.1. Ontology Design for Standardization
This study aimed to standardize instructional methods such that teachers with limited research
experience can understand the instructional methods of teachers with inquiry-based learning
instructional skills. In case of inquiry-based learning in Japanese high schools, there is a lack of
teachers with appropriate academic experience, a textbooks, and teachers of different subjects
teaching, etc. Therefore, there is a dearth of a support system to establish instructional methods.
One solution to this situation is to build a model of instructional methods for inquiry-based
learning. Therefore, we used ontology engineering, which is suitable for providing a common
language, rendering the implicit information explicit, and systematizing knowledge.
Ontology engineering, as proposed by Mizoguchi[9], is a theory and technology for repre-
senting essential conceptual structures that exist in reality on computers. Concepts defined
in an ontology can be used as a common concepts to represent knowledge and improve the
sharing and usability of knowledge.
The concept class based on the is-a relation, which is the key to ontology construction, is not
just a categorization class. It classifies concepts by clarifying the semantic differences between
concepts[10]. We considered that the differences in the state of inquiry learning can be classified
in inquiry-based learning instruction, which has been implicitly intellectualized.
2.2. Ontology and Education
Ontologies related to the field of education include: Ontology to support the design of com-
plex cooperative learning places in education[11], organization of theoretical knowledge[12],
ontologies for instructional design models[13], and support for searching instructional plans[14].
In the field of education, ontologies have been used to clarify tacit information and systematize
knowledge.
2.3. Ontology and Knowledge Acquisition Methods
Regarding knowledge acquisition methods for constructing ontologies, there are methods for
structuring general process knowledge[15] and expressing and describing inherent process
knowledge, such as the proposal of knowledge expression for extracting inherent process
knowledge from employees at nursing care facilities and the explicit description model of tacit
acts called CHARM[16][17]. In addition, there is a study on the construction of a domain
ontology in which procedural knowledge is constructed and tacit knowledge is acquired by a
classical guitar expert[18].
Referring to these studies, we decided to extract knowledge from two types of experts. One
is teachers in high schools who have enough experience in teaching inquiry-based learning.
The other is teachers in universities who have enough experience in research. We construct
a general process knowledge and evaluate the process knowledge based on the knowledge of
experts.
3. Modeling of the Instructional Process
3.1. Problem-solving Process
There exist certain problem-solving models. Among them, we chose the PPDAC cycle, which
is a statistical inquiry process that has been practiced in elementary, junior high[19], and
high school[20] in Japan. The PPDAC cycle is also used in New Zealand[21], where advanced
statistical education is provided. Therefore, we decided to construct procedural knowledge of
the problem-solving process based on the PPDAC cycle, which is used in education in countries
other than Japan and appears to be widespread in Japanese school education.
3.2. PPDAC cycle Overview
The five phases of the PPDAC cycle are Problem-Plan-Data-Analysis-Conclusion. In mathemat-
ics in the high school curriculum guidelines[4],the five phases of the PPDAC cycle consist of
Problem - Plan - Data - Analysis - Conclusion. The Problem phase involves understanding the
problem and setting it. The Plan phase assumes the data and plans the collection. The Data
phase collects the data and organizes them into tables. The Analysis phase creates graphs and
understands the characteristics and trends. Finally, the Conclusion phase involves drawing
conclusions and reflecting on the results. This renders it difficult to understand what should
be taught in each phase. Therefore, by extracting knowledge of the problem-solving process
and structurally describing procedural knowledge using systematized knowledge, a method for
teaching inquiry-based learning utilizing the PPDAC cycle becomes evident.
3.3. Knowledge Acquisition
Japanese high schools have a Super Science High School (SSH) , which aims to nurture the
ability to think through scientific inquiry and to develop internationally competent human
resources. Inquiry-based learning curriculum development and rubrics for evaluation have
been developed in SSH. A rubric is a list of evaluation criteria comprisin several levels of scales
and descriptive words. The rubric developed by SSH is an effective resource for acquiring
knowledge regarding the problem-solving process of inquiry-based learning. Therefore, we
used a rubric to extract knowledge about the state of inquiry-based learning.
In addition, standardized rubrics have been developed by eight SSH schools (standardized
rubrics)[22]. The standardized rubric describes the perspectives (evaluation criteria) and depth
of quality (evaluation standards) based on which the quality of students’ efforts is judged
in accordance with their problem-solving processes. The standardized rubric is also a useful
resource for procedural knowledge, as it includes a section referred to as ”Instructional Strategies”
that describes student behaviors and stumbling blocks that are found in the relevant criteria.
Therefore, we believe that it is effective for students to acquire knowledge of the problem-solving
process based on the standard rubric developed by SSH, which incorporates the practice of
advanced problem-solving practices.
3.4. Ontology Construction Steps
In this study, We adopted the following approach.
Step1 Knowledge related to the problem-solving process extracted.
Step2 Systematization of the extracted state of knowledge performed.
Step3 Structured procedural knowledge based on the PPDAC cycle.
In Step 1, terms related to the state of the problem-solving process were listed with reference
to the descriptive terms in the standard rubric. A comprehensive list of terms created without
considering the overlap between concepts, relationships between terms, or whether the term
represented a class or a property.
In Step 2, we systematized the knowledge obtained in Step 1. The lower classes were created
by specializing the upper classes using properties.
In Step 3, the knowledge systematized in Step 2 was structured based on the PPDAC cycle.
The status of each phase was assigned, that is, we set up the state as initial, intermediate,
evolving, and end states. We undertook structuring to consider the correspondence between
the state of inquiry-based learning and the phases of the PPDAC cycle.
3.5. Ontology Evaluation Methods
Experts with extensive experience in teaching problem-solving inquiry-based learning evaluated
the constructed ontology. In this study, we defined experts as those with at least four years
of experience teaching inquiry-based learning in high school and/or research experience in
university. We explained the PPDAC ontology to the Experts, who then responded to the
questionnaire based on the constructed ontology.
The questionnaire consisted of 21 items to evaluate the ontology. Table1 presents the ques-
tions. The responses had a five choice (1: no good, 2: not so good, 3: neither, 4: good to fair, 5:
good).
Table 1
Questionnaires for the experts
Q1 Readability of ontology
1-1 Did you understand the ontology overview of the PPDAC cycle
1-2 Do you understand classes and properties
1-3 Can you read the classification of the state of student understanding at the Problem phase
1-4 Can you read the classification of the state of student understanding at the Plan phase
1-5 Can you read the classification of the state of student understanding at the Data phase
1-6 Can you read the classification of the state of student understanding at the Analysis phase
1-7 Can you read the classification of the state of student understanding at the Conclusion phase
Q2 Appropriateness of ontology
2-1 Were the super-sub relation appropriate
2-2 Was the classification of process status appropriate
2-3 Was the definition of the Problem phase appropriate
2-4 Was the definition of the Plan phase appropriate
2-5 Was the definition of the Data phase appropriate
2-6 Was the definition of the Analysis phase appropriate
2-7 Was the definition of the Conclusion phase appropriate
Q3 Usefulness of ontology
3-1 Did the PPDAC cycle ontology enhance your understanding of procedural knowledge
3-2 Were the terms in the PPDAC cycle ontology helpful by structuring procedural knowledge
3-3 Was the process of procedural knowledge properly classified in the PPDAC cycle ontology
3-4 Was the PPDAC cycle ontology helpful in understanding the PPDAC cycle
3-5 Can you use this ontology to plan your instruction
3-6 Does this ontology enable you to conduct inquiry based learning
3-7 By using this ontology, can you formulate your evaluation criteria
4. Construction of PPDAC cycle Ontology
The PPDAC cycle ontology implemented on the ”Hozo” ontology editor[23].
4.1. Knowledge Extraction
The standardized rubric has four perspectives: setting the problem, planning and conducting
the research, gathering and evaluating information, and reflecting on the results. For each
perspective, the standardized rubric presents standards that represents steps to be achieved,
signs of the learner corresponding to the steps, and instructional strategies. Using these as
a reference, knowledge about the state of inquiry-based learning extracted and developed 93
concepts including 49 state concepts. We worked on the following. In terms of standards, we
conceptualized the ”question state” based on the descriptive words ”being able to formulate
a question” and the ”execution unknown state” and ”execution possible state” based on the
descriptive words ”being a plan with a view”. In terms of signs, ”verbalization state”, ” un-
considered state” and ”considered state” conceptualized based on the descriptor ”results and
consideration cannot be separated and only results are obtained”.
4.2. Knowledge Systematization
Based on the extracted knowledge and the standardization rubric, we defined the class-is-a
relationship in the states by setting properties for the upper and lower concepts. We defined and
specialized the following superclasses: ”formulation state,” ”hypothesis setting state,” ”research
readiness state,” ”data collection state,” ”analysis state,” and ”conclusion state.” The following is
a concrete example of systematization of knowledge about the formulation state based on the
standardized rubric. Figure1 shows the systematization of knowledge about the formulation
state.
First, it can be read from the descriptive words of Standardized Rubric, standard 2 ”Can
formulate a question” and standard 3 ”Can formulate a question or hypothesis based on the
goal of the research,” that the state transitions from the question state to the hypothesis state.
Second, from the descriptive words in Signs 3 ”Although it contains ambiguous words, it is
able to express what it wants to reveal through the research in the form of a goal or hypothesis,”
we set ”what it wants to reveal” as the ”expected solution” to the question. A boolean of
”presence/absence of expected solution” was set for the property, and if it was False, it was
structured as a ”question state” and if it was True, it structured as a ”hypothesis state”.
Third, the descriptive words in Signs 3 ”I have a hypothesis”, 4 ”I have a hypothesis based
on numerous experiments,” and 5 ”If there are previous studies, I have a problem that can be
compared with them” indicate that the state is ”hypothesis state” but is distinguished between a
hypothesis with an objective basis and a hypothesis without such a basis, and that the state is not
changed. The fact that the state does not change and that the hypothesis is not a hypothesis is a
distinction. As a result, we found that the state does not change, but there is a granularity in the
state. Therefore, we specialized the ”hypothesis state”, and set ”hypothesis setting/objectivity
yes” and ”hypothesis setting/objectivity no”.
By systematizing knowledge in this way, we systematized knowledge as ”hypothesis setting
state”, ”execution state”, ”data collection state”, ”analysis state” and ”conclusion state”. The
”hypothesis setting state” and the ”execution state” could only represent transitions of the states,
whereas the remaining states could represent the transitions and granularity of the states.
Figure 1: formulation state
4.3. Structuring Procedural Knowledge
Based on the PPDAC cycle, we structured the extracted knowledge.
First, ”process,” which was the upper class of each phase of the problem-solving process, was
assigned initial, intermediate, advanced, and end states. As the initial state and the end state
are states that must be reached, we set ”p/o 1”. Next, we set intermediate states for the relevant
phases because intermediate states may exist to facilitate transition from the initial to the end
state. Finally, the granularity of the states does not always result in a transition.
Therefore, for each phase, if there was a corresponding state, we set it as an advanced state.
The ontology constructed in this study posted on the site1 . Figure2 shows the structured
procedural knowledge of the Problem and Plan phases. Figure3 shows the structured procedural
knowledge of the Data, Analysis, and Conclusion phases.
Figure 2: Problem phase
1
https://sites.google.com/view/education-ontology
Figure 3: Plan/Data/Analysis/Conclusion phase
4.4. Ontology Questionnaire Results
Table2 presents the results of the questionnaire survey of high school teachers (n=5) and
university teachers (n=4).
All items were generally answered positively, with a high percentage of ”good” and ”good
to fair” responses. The results of the overall responses of high school and university teachers
showed that the only items with ”good” and ”good to fair” responses were 1-1, 1-2, 1-5, 2-
1, 2-5, 3-3, and 3-4. As evident, the readability of Overview, readability/appropriateness of
Class/Property, readability of Problem, and readability/appropriateness of Data in this ontology
were answered in the affirmative. The results also suggested that this ontology was effective
for understanding procedural knowledge, useful for structuring procedural knowledge, and
effective for improving the recognition of the PPDAC cycle. However, the readability and
appropriateness of the Plan, Analysis, and Conclusion phases were not positive, suggesting the
need for further study.
The responses of the high school teachers indicated, the items to which all five teachers
answered ”good” were 1-2, 1-7, 3-1, 3-2, and 3-4. The results of the high school teachers
responses were as follows. In terms of readability, the results suggested that the program was
effective in terms of understanding classes and properties an the classification of students’
comprehension status in the Conclusion phase. They were effective in terms of usefulness,
understanding procedural knowledge of the PPDAC cycle, raising awareness and recognition,
and usefulness of the ontology terms. However, items 1-6, 2-3, 2-6, and 2-7 were answered
as ”neither.” From the perspective of readability, the classification of students’ comprehension
states in the Analysis phase, and from the perspective of appropriateness, the definitions of the
states included in the Problem, Analysis, and Conclusion phases need to be examined.
The university teachers’ responses indicated, items for which only ”good” and ”good fair”
were given for both readability and adequacy in the Problem phase, ”Neither” and ”good fair”
for both readability and adequacy in the Analysis phase, and ”Neither” and ”good fair” for both
readability and adequacy in the Conclusion phase. The items that included ”neither” and ”not so
good” in both readability and appropriateness were the Plan, Analysis, and Conclusion phases.
5. Discussion
5.1. Ontology Evaluation
The results of the questionnaire for high school and university teachers were evaluated according
to their combined overall responses. Regarding the readability and appropriateness of the
proposed ontology, the readability and appropriateness of the Outline and Class Properties
in this ontology, and that of the Problem and Data phases in each phase of the PPDAC cycle,
received good evaluations. In the description of the questionnaire evaluation, there was a
positive opinion regarding knowledge extraction that high school teachers could recognize
that in the Problem phase, it is necessary to not only discover a problem, but also to facilitate a
transition from problem discovery to hypothesis formulation and solution proposal.
There was a positive comment regarding the systematization of knowledge that the transition
state of the inquiry activity was clearly expressed by the properties, and that what had been
tacitly taught was made explicit. In terms of the effectiveness of the PPDAC cycle ontology,
positive comments were observed on the understanding of procedural knowledge, usefulness of
the ontology terminology, and awareness of the PPDAC cycle as a result of the structuring of
procedural knowledge.
The high school teachers stated that the Data phase was not limited to simply acquiring
data, but that the objectivity, organization, and systematization of the data phase and the
developmental state of the PPDAC cycle made it effective in setting up instructional plans
tailored to the students’ level of understanding according to the school’s actual situation.
Further, it was clarified that the Problem phase was not merely setting a problem, and it was
Table 2
Results of Survey
All High school University
item 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
1-1 0 0 0 1 8 0 0 0 1 4 0 0 0 0 4
1-2 0 0 0 1 8 0 0 0 0 5 0 0 0 1 3
1-3 0 0 0 1 8 0 0 0 1 4 0 0 0 0 4
1-4 0 0 1 2 6 0 0 0 1 4 0 0 1 1 2
1-5 0 0 0 3 6 0 0 0 2 3 0 0 0 1 3
1-6 0 0 2 1 6 0 0 1 0 4 0 0 1 1 2
1-7 0 0 1 1 7 0 0 0 0 5 0 0 1 1 2
readability 0 0 4 10 49 0 0 1 5 29 0 0 3 5 20
2-1 0 0 0 3 6 0 0 0 2 3 0 0 0 1 3
2-2 0 0 1 4 4 0 0 0 4 1 0 0 1 0 3
2-3 0 0 2 2 5 0 0 2 1 2 0 0 0 1 3
2-4 0 0 1 1 7 0 0 0 1 4 0 0 1 0 3
2-5 0 0 0 5 4 0 0 0 2 3 0 0 0 3 1
2-6 0 1 1 4 3 0 0 1 2 2 0 1 0 2 1
2-7 0 0 2 0 7 0 0 1 0 4 0 0 1 0 3
appropriateness 0 1 7 19 36 0 0 4 12 19 0 1 3 7 17
3-1 0 1 0 0 8 0 0 0 0 5 0 1 0 0 3
3-2 0 1 0 1 7 0 0 0 0 5 0 1 0 1 2
3-3 0 0 0 3 6 0 0 0 1 4 0 0 0 2 2
3-4 0 0 0 0 9 0 0 0 0 5 0 0 0 0 4
3-5 0 1 0 4 4 0 0 0 2 3 0 1 0 2 1
3-6 0 1 1 2 5 0 0 0 2 3 0 1 1 0 2
3-7 0 1 0 2 6 0 0 0 1 4 0 1 0 1 2
usefulness 0 5 1 12 45 0 0 0 6 29 0 5 1 6 16
reaffirmed that it included setting a solution. Therefore, it became clear that considerable time
was required for instructional planning. The university teachers evaluated that the overall
picture of the teaching of inquiry-based learning was well presented and effective at the high
school level.
However, the Plan and the Analysis phases were evaluated to a certain degree in terms of
readability and appropriateness, but not sufficiently. Thus, they need to be reviewed. In the Plan
phase, it was reported that the properties of environmental factors in the state were abstract. It
is considered possible to specialize the environmental factors into two: the environmental factor
of whether data collection is possible and whether it is possible in terms of human resources.
In contrast to other phases, the Analysis phase does not simply transition from one state to
another. By clearly stating that the analysis state transitions according to the objectives, better
systematization can be achieved.
Therefore, although better improvements were suggested in the systematization of knowledge,
it can be judged that the proposed ontology has extracted the knowledge of inquiry-based
learning, systematized the state of inquiry instruction in terms of the relationship between
classes and properties, and structured the procedural knowledge of inquiry instruction based
on the PPDAC cycle in a favorable manner. The procedural knowledge of inquiry instruction
structured based on the PPDAC cycle.
5.2. Ontology validity
The validity was affirmed by comparing the results of questionnaires of high school and univer-
sity teachers.
In terms of readability, the visibility of the tacit knowledge, on both the Problem and Data
phases were positive, with the Analysis phase being generally positive, although there was one
”neither agree nor disagree” respondent each. However, the high school teachers were positive
about the Plan and Conclusion phases, whereas the university teachers included ”neither” in
their responses. University faculty members indicated that it was difficult for them to understand
the terminology of the labels for the Plan and Conclusion phases. This is a problem of the
notation of the labels, which can be improved immediately, and is not an essential problem for
knowledge extraction and systematization.
In terms of appropriateness, both Data were positive, while the Analysis and Conclusion
phases each contained one non-affirmative response. The Problem and Plan phase questions,
answered by the high school and university teachers each contained a negative response. In the
Analysis phase, both the high school and university teachers suggested points for improvement
by setting the purpose to the properties of the analysis phase. In the Problem phase, it was
suggested that it would be better to add a specific systematization of knowledge regarding
the reality state survey, which is a property of the formulation state. In other words, the
procedural knowledge of the reality state survey should be expressed using the knowledge
extracted in this study. The Plan phase was an additional opinion regarding the particularization
of environmental factors as well as readability. Therefore, the factors of the non-positive
answers were not related to the extraction, systematization or structuring of the knowledge
of this ontology: rather, they were related to the results of the answers that indicated better
improvements.
In terms of usefulness, the results of the responses differed greatly between high school and
university teachers. This is because procedural knowledge about the Problem and Analysis
phases are not a one-way process in inquiry-based learning (research guidance) in university
education, but is implemented through trial-and-error, and the expression of this knowledge
is insufficient. However, it was found that the ontology was sufficiently useful in high school
education if the inquiry-based learning of high school education were regarded as an introduc-
tion to the research activities of university education. In other words, the respondents were not
positive about the effectiveness of the ontology as an ontology to be utilized up to university
education; however, if it was limited to high school education, its effectiveness was evident.
Therefore, although there were concerns regarding readability, appropriateness, and the
validity of the ontology, the survey of the factors that were answered by the respondents
indicates that they were no factors that were considered inappropriate for this ontology, and
that they were generally not affected by the ontology.
6. Conclusion
In this study, we constructed a PPDAC cycle ontology for the inexperienced teachers of inquiry-
based learning using the problem-solving process. Based on a standardized rubric, we extracted
and systematized the knowledge about the problem-solving process. Referring to the process
model, we developed the PPDAC cycle ontology related to the problem-solving process. Further,
we evaluated the ontology through consultations with experts in the field of education at
both high schools and universities and found that the PPDAC cycle ontology exhibited certain
evaluation and validity. Therefore, we clarified the following four points.
• We extracted and rendered the knowledge about the detail of the problem-solving process
explicit.
• Systematized knowledge, that is, PPDAC ontology rendered tacit teaching methods
explicit.
• The PPDAC ontology should support inexperienced teachers to plan their instruction for
inquiry-based learning using the PPDAC cycle.
• The PPDAC ontology revealed that the Problem phase comprised many states and pro-
cesses, which requires sufficient guidance and time, particularly for the inexperienced
teachers.
The proposed ontology constructed in this study should help both the computer and the
inexperienced teachers to understand the overall view in the teaching of inquiry based learning
and helps the computer system to reason about the teaching methods and/or class planning for
the teachers.
In the future, Two points to be discussed are as follows. The first is to verify that inexperienced
teachers can use the system for planning inquiry-based instruction. The second is to construct
a system that can automatically create an annual instructional plan for inquiry-based learning,
as shown in Figure 4.
Figure 4: an automatic creation system in the annual instructional plan for inquiry-based learning.
Acknowledgments
This work was supported by the New Energy and Industrial Technology Development Organiza-
tion (NEDO) JPNP18002. We would like to thank Editage (www.editage.jp) for English language
editing.
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