Automatic Question Generation With Classification
Based On Mind Map
Selvia Ferdiana Kusuma
Informatics Department
Institut Teknologi Sepuluh
Nopember
Surabaya, Indonesia
selvia1805@mhs.its.ac.id

Daniel Oranova Siahaan
Informatics Department
Institut Teknologi Sepuluh
Nopember
Surabaya, Indonesia
daniel@its.ac.id

Chastine Fatichah
Informatics Department
Institut Teknologi Sepuluh
Nopember
Surabaya, Indonesia
chastine@its.ac.id

Mohammad Farid Naufal
Informatics Department
Universitas Surabaya
Surabaya, Indonesia
faridnaufal@staff.ubaya.ac.id

through the predetermined grid provided by their teacher. Then
they extend the predetermined grid to predict other questions
that might come out on USBN. But, to predict other questions
is not easy. Students must understand the learning material
then they can make questions about that.

Abstract— In Indonesia’s national education system, a
question within a national standardized school examination
(Ujian Sekolah Berstandar Nasional, USBN) is generated based
on a predetermined grid. A predetermined grid is a matrix that
contains a set of criteria that all question should abide. Students
who would like to take the final school exam usually prepare
themselves by learning through the existing predetermined grid
then extend their knowledge around the predetermined grid. But
extending knowledge around the predetermined grid is not easy
because it depends on the student's creativity to associate the
material that exists. The contribution of this research is to
generate various types of questions that have relevance to the
predetermined grid. The dataset that used is learning the
material of all grade in 4, 5, and 6 of elementary school science
subjects. The question generation process is done by the rule
which generated from the mind map. The mind map will help to
determine the interrelationship between the learning material.
This study uses 50 competencies from the predetermined grid as
input to generate questions. The results of question generation
are then validated by an expert. Expert state that all inputs used
can generate questions according to competence. The experiment
result shows that the proposed method can be used to generate
questions that are related to the predetermined grid.

Currently, the questions in USBN have several difficulty
levels. Questions with varying difficulty level can be used to
assess students' abilities more precisely [4]. The varying
difficulty level is divided into three levels, namely Lower
Order Thinking Skill (LOTS), Medium Order Thinking Skill
(MOTS), and High Order Thinking Skill (HOTS). The first
level is LOTS. LOTS means the students can remember and
understand a learning material. For example, a LOTS question
is "What is perfect metamorphosis?". This question is
considered as having LOTS level because to answer it, a
student needs to understand the learning material about perfect
metamorphosis. The second level is MOTS. MOTS means the
students can applicate a learning material. For example, a
MOTS question is "Animals classified as perfect
metamorphosis are…". This question is considered as having
the MOTS level because to answer it, a student needs to apply
their knowledge to classify animals that carry out perfect
metamorphosis. The third level is the HOTS. HOTS mean
students have reasoning abilities for learning material. For
example, a HOTS question is, “Are there factors that influence
perfect metamorphosis? Try to explain your opinion!”. This
question is considered as having the HOTS level because to
answer it, a student needs to provide an analysis of what
factors will affect the perfect metamorphosis process [5].
HOTS questions are usually made from a combination of
previous learning material. Therefore students are also required
to understand the interrelationships between learning material.

Keywords—Question Generation, Rule-Based, Mind Map,
Question Classification

I. INTRODUCTION
In Indonesia, the questions for the final exam are developed
through a predetermined grid [1]. A predetermined grid is a
matrix that contains a set of criteria that all question should
abide. The purpose of making a predetermined grid is to
determine the scope and as instructions for writing questions
[2]. The predetermined grid consists of competency, material
scope, material, cognitive level, question indicator, the form of
a question, and a number of the question [3]. Formal education
in Indonesia consists of elementary school, junior high school,
senior high school, and university. An elementary school has
six levels. Start from grade 1 until grade 6. Students in the sixth
grade of elementary school should pass a national standardized
school examination (Ujian Sekolah Berstandar Nasional,
USBN) to be able to continue to the junior high school level.
Questions of USBN for elementary schools in Indonesia are a
combination of learning material in grades 4, 5, and 6.
Therefore, to passed USBN, students are required to remember
and also linking their knowledge from learning the material in
grades 4, 5, and 6. Students who will face USBN usually learn

Latipah and Adman [6] using a mind map to improve
learning outcomes. Mind map could help students to find out
the important points of discussion in the learning material [7].
There has been no other study where mind map combines
similar materials from different levels of grades. Recently,
mind maps are only made for each chapter or subchapter. It
will only help students to understand the points one by one, but
not the interrelationship, and the question that may arise from
the learning material. Currently, mind maps are just learning
media. Although there is a possibility that mind maps can also
be used to help make learning evaluation questions
automatically.

978-1-7281-0719-6/19/$31.00 ©2019 IEEE
336

Kusuma and Alhamri [10] make the question generation
classified according to the taxonomy of bloom. The method
used is template based. This research does not only generate
questions but also classifies the level of difficulty. The
classification process is based on the keywords that appear on
the question. In addition to questions, this study also predicts
answers based on the template that has been made. But the
question generation in this study is very dependent on the
sentence inputted. This study cannot generate questions outside
the inputted sentence.

Recently, there have been several studies using both
Indonesian and English that try to generate questions
automatically. The research included the question generation of
5W 1H for the domain of Indonesian using syntactical
templated-based features from academic textbooks [8],
generating automatic type questions for Indonesian texts that
use compound sentences [9], and generating questions in
Indonesian based on Bloom's Taxonomy using template-based
methods [10]. There are also three studies related to the
question generation that use English, namely automatic/smart
question generation system for the academic purpose [11],
automatic question generation from paragraph [12], and
automatic generation-based questions on analysis of sentence
structure [13]. The majority of research that has been carried
out only focuses on generating questions according to the
sentences or paragraphs that are used as input. The question
raised is dependent on the input sentence. There is still rarely
research that can generate questions outside the input sentence
but still have relevance to the sentence used as input.

Vibahndik and Samant [11] made the generation of
questions in the field of Education. There are seven stages used
to generate a question. The seven stages are Key Phrase
Extraction, Key Phrase Classification Conceptual Graph
Construction and Conceptual Question Generation,
Abbreviation Extraction, Possible Option Extraction, and MCQ
generation. This research only focuses on five categories of
education, namely Research Field, Technology, System, Term,
and others.

The variety of types of questions commonly used for
evaluating learning outcomes requires that automation of
question generation in the field of education can generate
questions with the varying difficulty level. To generate
questions with varying difficulty level cannot only depend on
input sentences but also must understand the relevance of the
input sentences with the other topics. Therefore this study
focuses on generating questions outside of input sentences but
having relevance to sentences that are used as input. This study
uses a mind map to see the interrelationships between topics.
The existence of a mind map helped to increase the variety of
questions.

Swali, Palan, and Shah [12] do the generation of questions
from a paragraph using a semantic approach and a syntactic
approach. Each sentence will be selected first, whether it can
be a question or not. The selection process is done by
identifying the features of each sentence. The features used are
the first sentence, last sentence, frequently appearing words,
sentence length, number of nouns, adverbs, and conjunctions.
This study has not been able to make questions from sentences
that have pronouns.
Blstak & Viera [13] performs the question generation
through the template-based method obtained from sentence
structure analysis. This study produces a more effective pattern
than previous studies because it has fewer patterns but has
better results in generating problems. The pattern in this study
was made through a combination of POS Tags, Named Entity
Relations (NER), and Super Sense Tags (SST). Although it has
good accuracy, the use of combining patterns makes the
computing process more complex.

This paper is organized as follows. In section 2, we have
described the previous work. In section 3, we have introduced
the implementation of our approach. In section 4, we have
evaluated the result. In section 5, we have described the
conclusions of this method and future work.
II. PREVIOUS WORK
Setio & Kusuma [8] made the generation of questions for
the academic field. Four processes are carried out to generate
these questions including the identification of declarative
sentences for eight co-classes and 19 fine-class sentences, the
classification of rules for fine-class sentences, the identification
of question patterns and the extraction of sentence components
as well as the rule generation of questions. This research
resulted in an accuracy of 83% for coarse class and 92% for
fine class.

III. RESEARCH METHODOLOGY
In this section, we have described our approach in detail.
There are three processes in this study, namely dataset
collection, mind map building, and question generation and
classification.
A. Dataset Collection
The dataset used is elementary schools science learning
materials of class 4, 5, and 6. The material is obtained from the
Buku Sekolah Elektronik (BSE) of the Indonesian Ministry of
Education. The material has been used as a material for making
mind maps.

Ginanjar and Purnamasari [9] make automatic question
generation for Indonesian Texts containing compound
sentences. The method consists of two stages, namely preprocessing and main processing. Pre-processing is the initial
stage for getting word types and entities named for each word
in the sentence, while the main process is a syntactic analysis
process which consists of identifying phrases with pattern
matching methods, the process of determining grammatical
functions with rule-based methods and the question generation
process with the template-based method. The results of the
problem generation accuracy are 71.06%.

B. Mind Map Building
Mind maps are built based on discussion points in a
material. Figure 1 shows an example of a mind map of the
animal life cycle.

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Animal Life Cycle

Metamorphosis
Perfect
Metamorphosis
Butterfly

2.1.1.1
Omnivora
2.1
Penggolongan
hewan

Non-Metamorphosis

Non-Perfect
Metamorphosis

Fish
Lizard

Kelas 4
2. Hewan

Cockroach

2.2
Proses daur
hidup hewan

Kelas 5

2.1.1
Terdiri dari

2.2.1
Definisi
2.2.2
Terdiri
dari

Kelas 6

2.3
Organ
pernapasan
khusus
2.4
Ciri khusus
pada hewan

2.5
Perkembangbiakan
hewan

2.2.2.Y
Definisi

2.2.2.1
Metamorfosis Semppurna
2.2.2.2
Metamorfosis Tidak Sempurna

2.2.2.Y
Contoh hewan

2.3.1.Y
Alat pernap asan
2.3.1.Y
Proses pernapasan
yang terjadi

2.3.1
Contoh

2.3.1.1 Burung
2.3.1.2 Serangga
2.3.1.3 Cacing
2.3.1.4 Ikan

2.5.1
Terdiri dari

2.3.1.Y
Urutan proses
pernapasan
2.3.1.Y
Faktor yang mempengaruhi
pernapasan
2.3.1.Y
Perbadaan antara pernapasan
(X1) dan (X2)

2.4.1 Contoh

2.5.1.2
Generatif

Mind maps are formed from a collection of discussion
points, grade, sub-discussion points, and issues. Fourth
components come from learning material at BSE. Discussion
point comes from the material in BSE. The grade comes from
the level in elementary school. The sub-point discussion comes
from the sub-material discussed, while the issue is formed from
the questions that appear in the final evaluation section in the
material/discussion point. Each discussion point is related to
sub-discussion points. Linkages can occur between subdiscussion point in one class or different classes (4.5, or 6). The
sub-discussion point grouping is important because sometimes
there is a connection between sub-discussion point that we
must know, for example before understanding breeding in
animals, students should know about the theory of animal life
cycle processes, animal classification and other theories related
to animal life. Experts will validate the truth of the formation
of discussion points, sub-discussion points, issues, and
grouping of discussion points. Examples of discussion point
are "2. Hewan” (2. Animal). Example of grades is 4,5 or 6.
Example for the sub-discussion is "2.1Penggolongan hewan"
(2.1 Animal Classification). The example for the issue of the
sub-discussion is in point "2.1.1Terdiri dari" (2.1.1 consists of).
The issue was formed because, at the end of the evaluation,
there was a question "Classification of animals consisting of
...".
The extended mind map concept in Figure 2 looks almost
the same as ontology-based knowledge modeling as in the
research of Le Thanh, Matokhina, and Kizim [14] who use
ontology to model knowledge in the domain of "Electrical
Engineering." But actually, ontology is not suitable if applied
in this study. The concept of ontology is to provide
relationships between each class using the property. In this
study, knowledge is divided into four parts, namely discussion
points, grades, discussion sub-points, and issues. Subdiscussion points always have issues that represent questions
that can be raised from the sub-discussion. The issue does not
explain the relationship between sub-discussions. So, the
concept cannot be equated with ontology

2.1.1.Y
Contoh

2.1.1.3
Herbivora

2.2.2.3
Tidak Metamorfosis

Figure 1. Mind Map of Animal Life Cycle

From the main discussion point, the mind map of the animal
life cycle will branch out into smaller sub-discussions. There
are no specific rules or limitations in making mind maps.
Therefore, one mind map can have various versions. The
majority of mind maps do not bring up issues that can be used
as questions from the material. Therefore, the mind map used
in this study was modified. It focuses on generating mind maps
to generate a question based on the material. The mind map in
this study would also be modified so that it has material
relevance in the previous class. An example of an extended
mind map used as a dataset is shown in Figure 2.

2.1.1.2
Karnivora

2.1.1.Y
Definisi

2.5.1.1
Vegetatif
2.5.1.2.1
Definisi

2.5.1.1.1 Definsi
2.5.1.1.2 Contoh
2.5.1.1.3
Proses perkembangbiakan

2.5.1.2.1
Contoh

2.5.1.2.3.1
Vivipar

2.5.1.2.3
Terdiri dari

2.5.1.2.3.2
Ovipar
2.5.1.2.3.3
Ovovivip ar

2.5.1.2.3.Y Definisi
2.5.1.2.3.Y Contoh
2.5.1.2.3.Y Proses
Perkembangbiakan

Figure 2. Extended Mind Map of Animal (Hewan)

After there are sub-points of discussion there will be an
issue related to the sub-points of the discussion and so on. The
issue "2.1.1Terdiri dari" (consisting of) will produce the next
sub-discussion point, which is divided into omnivores,
carnivores, herbivores. From the second sub-discussion point,
new issues will arise regarding “2.1.1.1Y Definisi” (2.1.1.Y
Definitions) and “2.1.1.Y Contoh hewan” (2.1.1.Y An example
of an animal).
The process of making mind maps is shown in Figure 3.
The process is done manually by an expert in the field of
education. The expert will collect material along with
supporting questions from each material for grades 4, 5, and 6.
Then the expert will raise the issue of each material based on
the questions that are often raised from the material. The issues
that arise will be formed like a mind map. Rule generation
questions will be developed based on the mind map that is
formed. Examples of rule from mind maps in Figure 2 are
shown in Table 1. Rules that are made are general, which
means they can be applied to any material. For example, for
the issue of “2.3.1.Y Proses yang terjadi pada (X)” (The
process that occurs in (X)), the issue can be used to generate
questions from material about “2.3.1.1 Burung” (Bird) and
“2.3.1.3 Ikan” (Fish). The example of the question is, “How do
the process that occurs in Bird breathing?” And “How to do the
processes that occur in Fish breathing?”. The symbol (X) is the
sub-discussion point from the previous branch. The symbol (Y)
in each issue means that the issue can be used to generate
questions from sub-discussion points in the previous branch.
Besides generating rules, experts will also determine the
difficulty level of the questions in every rule. Classification on
the difficulty level is based on Bloom's Taxonomy. Capability
for remembering (C1) and understanding (C2) are classified
into LOTS level. Capability for applying (C3) is classified into
MOTS level. While Capability for analyzing (C4), evaluating

338

TABLE I. RULE IDENTIFICATION
No
1

Level
L

2

3

H

H

Issues
2.1.1 Terdiri dari..
(Consist of)
2.3.1.Y Proses yang
terjadi
(Processed Occurred)
Hubungan antar
materi pelajaran.
(Linkages between
materials)

Rule
(X) terdiri dari...
((X) Consist of..)
Bagaimana proses yang
terjadi pada (X)?
(How is the process that
occurs at (X)?)
Apakah ada hubungan
antara (X) dan (W)?
(Is there a link between (X)
and (W)?)

Generated Questions
Questions 1:Penggolongan hewan terdiri dari… (Animal classification consists of…)
Questions 2:Perkembangbiakan hewan terdiri dari…(Animal breeding consists of…)
Question 1: Bagaimana proses yang terjadi pada pernapasan burung? (How is the process
that occurs at bird breathing?)

Question 2: Bagaimana proses yang terjadi pada pernapasan ikan?(How is the process
that occurs at Fish breathing?)

Question 1: Apakah ada hubungan proses daur hidup hewan dan perkembangbiakan
hewan? (Is there a link between animal life cycle and breeding of animals?)
Question 2: Apakah ada hubungan antara organ pernapasan dan ciri khusus pada
hewan? (Is there a link between respiratory organs and special characteristics in animals?)

(C5), and creating (C6) are classified into the HOTS level [2].
The difficulty level for each rule will be shown in the level
column. L means LOTS, M means MOTS, and H means
HOTS. There is an issue that is not directly reflected as a
branch of sub-discussion point, for example, in table 1 no 3.
The issue of the relationship between materials appears not yet
reflected as a branch because this issue will always be
generated automatically from 2 sub-discussion point.

The difficulty level for each question raised has been
determined during the process of making mind maps and rules
for each issue on the mind map. Details of each process shown
in Figure 3 will be explained in the following sections.
1.

The input in this study is a competency. Competency is
one part of the predetermined grid. An example of competency
is “Siswa dapat menjelaskan tentang proses pernapasan pada
burung” (Students can explain the breathing process in a bird).
In accordance with the steps in Figure 3, the input then goes to
the preprocessing stage. Preprocessing begins with
tokenization, stop word removal, and stemming. Tokenization
is the separation of sentences into word to word. The results of
the tokenizing process are “Siswa-dapat-menjelaskan-tentangproses-pernapasan-pada-burung." After tokenizing then going
to the stop word removal process. Stop word removal is used to
remove non-essential words. The non-essential words referred
to in this study are words that do not represent related topics in
this discussion point. The result after the stop word removal is
“proses-pernapasan-burung." The word “siswa” (student) is not
included in the stop word, but the words are added to the stop
word because the word is considered meaningless in this study.
The next step is the lemmatization process. Lemmatization
means getting the basic word from a word. The result of
lemmatization process is “proses-napas-burung." The results of
the lemmatization process are the terms that will be used as
input in the process of keywords matching.

C. Question Generation and Classification
The concept of the question generation process from
beginning to end is shown in Figure 3. The generation of
questions begins by entering the competency as input. Then the
competency is entered into the preprocessing, which consists of
tokenizing process, stop word removal, and lemmatization. The
term from preprocessing result will be compared with the
words in the mind map. When there are the same words, mark
the sub-discussion points. Then find the suitability of the issue
based on the selected sub-discussion points. Use the rule in
each issue that is selected to generate questions. The results of
this method are questions that have different difficulty level but
still have a relationship of discussion with the competency used
as input.
Competency

Preprocessing
Tokenizing

List of
StopWord

Stopword Removal

Mind Map Building
Learning Materials
and Questions

2.

Generate Issue from
Paragraph and Questions
Expert

Keyword Matching
Compare term and word on
mind map
Selected sub
discussion point
Choosed the rule based on
selected sub discussion poin

Selected rule

Keyword Matching

Keyword matching is done to find the appropriate
discussion points and rules on the mind map. Two processes
occur at the keyword matching stage. First is comparing the
terms and words in the mind map to determine sub-discussion
points, and the second is rule selection based on the selected
sub-discussion points. These rules will be used as a benchmark
for generating questions. In the first process, each input in the
keyword matching process will be matched with a discussion
point from the leftmost mind map. When the word is not found
in each discussion point, then searches for the second branch of
each sub-discussion point (marked with 2-digit number code)
and so on. But if the word is found, it will focus only on the
appropriate sub-discussion point. For example, the keywords
used are "proses-napas-burung" (process-breath-bird). The
words are found in code 2.3 for “breath”, 2.3.1.1 for “bird” and
2.3.1.Y, 2.5.1.1.3, 2.5.1.2.3.Y for “process”.

Lematization

term

Preprocessing

Generate Mind Map & Rule with
Difficulty Level Classification

Mind Map & Rule
with Difficulty Level
Classification
Mind Map
And Rule

Question Generation and
Classification
Generate Questions With
Classification Based On Issue On
Mind Map

Questions With
Classification Based On
Issue On Mind Map

Figure 3. Question Generation Process

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TABLE II. RULE OF QUESTION GENERATION
No Level
1

L

2

L

3

M

4

H

5

H

Issues
(2.3.1.Y)
Alat pernapasan
(Respiratory)
Proses pernapasan yang
terjadi
(The breathing process
that occurs)
Urutan proses
pernapasan
(Sequence of breathing
process)
Faktor yang
mempengaruhi proses
pernapasan
(Factor that effects on
breathing process)
Perbedaan antara dua
proses pernapasan
(The difference
between 2 breathing
process)

Question in Indonesian
Soal untuk materi pernapasan burung:
1. Pernapasan Burung menggunakan…
2. Faktor yang mempengaruhi proses pernapasan Burung adalah…
3. Proses yang terjadi pada pernapasan Burung adalah…
4. Urutan proses pada pernapasan Burung adalah....
5. Perbedaan antara pernapasan Burung dan Serangga adalah…
Question In English
Questions for discussion point of bird breathing:
1. Bird breathe with…
2. Factors that affect the process of bird breathing is…
3. A process which is happened on bird breathing is…
4. The sequence process of bird breathing is…
5. The difference between bird breathing and the insect is…

Rule
(X) bernapas dengan…
((X) breathe with…)
Proses yang terjadi pada
pernapasan (X) adalah…
(Process which is happened (X)
is…)
Urutan proses penapasan (X)
adalah…
(Sequence of breathing process on
(X) is...)
Faktor yang mempengaruhi proses
pernapasan (X) adalah…
(Factor that effect on breathing
process (X) is…)

Figure 4. Result of Question Generation and Classification

The generated rule is equipped with the difficulty level. So
that when using the rule, it will be known what level the
question is. Question number 1 in Figure 4 is the question of
remember level (LOTS) because to answer it, students have to
remember the material that has been studied. Question number
4 is a question of apply level (MOTS) because to answer it, the
student has applied theory about respiratory of a bird. Number
3 is the question of understanding level (LOTS) because to
answer questions, students only should explain the
understanding. Whereas questions number 2 and 5 are a
question of analyzing level (HOTS) because the answers to
these questions are not in the material being studied. So, the
student must analyze by their self. The division of the difficulty
level of this question is based on Bloom's Taxonomy.

Perbedaan antara pernapasan (X1)
dan (X2) adalah…
(The difference between the
breathing of (X1) and (X2) is ...)

The word "process" is located in more than 1 subdiscussion point. If there are words detected in some subdiscussion point, the word will be ignored. That makes the
focus of the sub-discussion point for generating questions, not
false. After getting the sub-discussion points then proceed to
the second process, namely rule selection based on the selected
sub-discussion points. Every sub discussion point always has
an issue. The issue is always after the sub discussion point.
Every issue has a rule, so the rule will be found if an issue has
been found — the example rule used in this study, as shown in
table 2. The (X) represents a sub-discussion point in the
previous branch. If there are two sub-discussion point
compared, then codes X1 to Xn will be used.
3.

IV. RESULT AND DISCUSSION
From this study, it is known that to be able to generate a
question at the HOTS level, it cannot be made simply by using
descriptive sentences from a material. But also need to use the
linkages of the information that is available to form HOTS
questions. Therefore, to find out the interrelationship between
learning materials, a mind map is needed. The mind map will
help to see the relationship between words and learning
materials. There are eight discussion points in the mind map
that are used as datasets. There are human, animal,
environment, objects, universe, energy, and force (Manusia,
hewan, lingkungan, benda, alam semesta, energy, dan gaya).
The eight discussion points produced 412 issues. Completeness
of mind map will affect the number of issues. The more issue
of the mind map, the more questions that can be generated.
Figure 5 shows a comparison chart between the number of
discussion sub-points and the number of issue of questions that
can be raised. The majority of data shows that the generation of
the issue is twice as large as the discussion point. The X axis
on the chart represents the discussion point while the Y axis
represents the number of issues from each discussion point.

Question Generation and Classification Process

The generation of questions is based on the rule that
appears. Examples of questions that can be generated from the
rule in Table 2 are shown in Figure 4. If the rule “(X) breathe
with... ” then the symbol (X) will be replaced with the previous
sub-discussion point. In this case, the sub-discussion point is a
bird. So the question that will be formed is "Bird breathes
with… ”. From point 2.3 about respiratory organs, it is known
that respiratory organs material in animals not only discusses
the respiratory of bird but also discusses the respiratory of
insect, worms, and fish. So, the rules that were used to generate
questions related to respiratory of bird can also be used to raise
questions regarding the respiratory of insect, worms, and fish.
The results of the generation of questions for respiratory of bird
in animals are shown in Figure 4. Sub-discussion about the
respiratory organ of animal has produced 20 questions. But the
example in Figure 4 only produces five questions because it
only raises questions for 1 type of animals, namely bird. The
same rule can be used to generate questions for similar subdiscussion point at the same level.

340

HOTS level questions are still limited. Therefore, there must be
another method used to generate questions at the HOTS level.
Besides that, the next research will also focus on developing a
more varied and dynamic generate question method.
ACKNOWLEDGMENT
The authors say thank you to the Ministry of Research and
Higher Education for supporting this research and all of the
participants who have helped in the process of collecting
datasets, classification, and validation in this research.

Figure 5. Comparison Between Discussion Points And Number of Issue
R i d

REFERENCES
[1]

[2]
Figure 6. Comparison of The Number of Questions For Each Difficulty
Level

[3]
[4]

Figure 6 shows a comparison chart between the number of
questions that can be raised at each level. The X-axis on the
chart shows the discussion point while the Y-axis represents
the number of questions at each difficulty level from each
discussion points.

[5]

[6]

From these data, it can be seen that LOTS level produces
the highest questions, and HOTS level produces the fewest
questions compared to other levels. These happened because
the rule of generating questions at HOTS level that has been
successfully modeled is still limited. There must be another
method used to generate questions at the HOTS level. The
experiment in this study uses 50 competencies from the
predetermined grid as input. And then competency produced
questions similar to the competency. The result of question
generation validated by an expert. Experts validate by
comparing questions generated by experts with questions
generated by this method. If it is the same, it will be marked
“yes," but if not, it will be marked “no." The number of “yes”
answers will be divided by the total number of questions
raised. The calculation will represent the accuracy of the
success of the proposed method. An expert who carries out
validation of generated questions suggests that all of the
questions are related to the competency used as input.

[7]

[8]

[9]

[10]

[11]

[12]

V. CONCLUSION
[13]

Mind maps will help determine the interrelationship
between the learning material. Mind maps can be used to help
generate questions automatically and with the various difficulty
level. The more branch the mind map has the more questions
that can be generated. There are 412 issues that can be used as
material for generating questions. But from the 412 issues, only
56 issues were categorized at the HOTS level. From this study,
it is known that the rules that have been modeled to generate

[14]

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