Preface: The 2nd Science and Mathematics International Conference (SMIC 2020)

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AIP Conference Proceedings 2331, 010001 (2021); https://doi.org/10.1063/12.0003237 2331, 010001

Preface: The 2nd Science and Mathematics International Conference (SMIC 2020)

Cite as: AIP Conference Proceedings 2331, 010001 (2021); https://doi.org/10.1063/12.0003237 Published Online: 02 April 2021

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Preface: The 2

^nd

Science and Mathematics International Conference (SMIC 2020)

We are delighted to present the proceeding of the 2

^nd

Science and Mathematics International Conference (SMIC 2020). The conference was organized by the Faculty of Mathematics and Natural Sciences, Universitas Negeri Jakarta, Indonesia, on 8 – 9 August 2020. The theme of SMIC 2020 was “Transforming Research and Education of Science and Mathematics in the Digital Age.”

The scopes of this conference covered mathematics, general physics, chemistry, biology, computer science and technology. SMIC 2020 has brought together leading academics, researchers, multi- disciplinary groups of scientists and practitioners from different countries to present and exchange ideas relating to mathematics, science, its application, the future trends and needs.

We received 269 registrations, the participants have submitted and presented their paper in SMIC 2020. After conducting a series of reviewing process, 173 papers were accepted for publication.

The authors were from Egypt, Estonia, Ghana, Indonesia, Malaysia, Philippines, Republic of Korea, Singapore, Taiwan, and USA. The papers selected for this proceeding provide insight into the development of research and education in science and mathematics in the digital age.

Finally, we also like to express our gratitude to the SMIC committee members for their hard work and dedication.

Thank you.

Jakarta, October 10

^th

, 2020

The SMIC Head Committee Dr. Mutia Delina

The 2nd Science and Mathematics International Conference (SMIC 2020) AIP Conf. Proc. 2331, 010001-1–010001-1; https://doi.org/10.1063/12.0003237

Published by AIP Publishing. 978-0-7354-4075-3/$30.00

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Committees: The 2nd Science and

Mathematics International Conference (SMIC 2020)

Preface: The 2nd Science and Mathematics International Conference (SMIC 2020) AIP Conference Proceedings 2331, 010001 (2021); https://doi.org/10.1063/12.0003237

The application of learning cycle 7E model to improve a mathematics communication skills of junior high school students

AIP Conference Proceedings 2331, 020001 (2021); https://doi.org/10.1063/5.0041644 Dynamic modelling of Hepatitis B and use of optimal control to reduce the infected population and minimizing the cost of vaccination and treatment

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The 2

^nd

Science and Mathematics International Conference (SMIC 2020) Transforming Research and Education of Science and Mathematics in the Digital Age

Jakarta, Indonesia 8-9 August 2020

Editors:

Meiliasari ([email protected])

Yuli Rahmawati ([email protected]) Mutia Delina ([email protected]) Ella Fitriani ([email protected])

Faculty of Mathematics and Natural Science Universitas Negeri Jakarta

Rawamangun, Jakarta Timur, 13220 Indonesia

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CONFERENCE DETAILS

Organizer : Faculty of Mathematics and Natural Science, Universitas Negeri Jakarta Email: [email protected]

Website: http://fmipa.unj.ac.id/smic2020/

The Committees Steering Committee

1. Dr. Adisyahputra, M.S Dean, Faculty of Mathematics and Natural Science

2. Prof. Dr. Muktiningsih, M.Si Vice Dean of Academic Affairs 3. Drs. Sudarwanto., M.Si., DEA Vice Dean of General and Financial

Affairs

4. Dr. Hadi Nasbey, M.Si Vice Dean of Alumni, Students and Cooperation Affairs

Organizing Committee

Head Committee Dr. Mutia Delina

Secretary Dr. Umiatin, M.Si

Treasury Sri Rahayu, M.Biomed

Wiwik Endang Sulistiyowati, S.Pd Isfi Zahara

Afrilisa Nur Rosifa Gusverizon Secretariat, Public Relation, and

Registration

Ella Fitriani, M.Pd

Dewi Muliyati, M.Si., M.Sc Agus Agung Permana, S.Si Samuel Yesaya Wirjopranoto Amarta Prayuti

Ali Sabeni Naufal Ma’arif

Loviya Azzahra Yahya Lismu Dhita Septiyaningrum Ihsan Hijria Putra

Drajat Agung Nugroho Vidya Kusumah Wardani Raffa Fitra Ramadannisa Benedikta Lorenza Dheanti Muhammad Afandi

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Conference and Workshop Session

Dr. Hanhan Dianhar, M.Si Dania Siregar, M.Si

Publication Yuli Rahmawati, Ph.D

Dr. Meiliasari, S.Pd., M.Sc Fitri Khairunnisa

Tri Fauji Ratna Maryam Salsabilla Aurani Mario Aditya Prasetyo Atikah Aulia Putri

Sponsorship Vera Maya Santi, M.Si

Upik Rahma Fitri, M.Pd

Equipments Zainul Ali, S.Kom

Achmad Ainul Yaqin, S.T Programme Division Valendio Febriano

Ferdy Alfian Indra Prasetya Huzaifi Hafizhahullah Wahyu Dwi Meilianto Daffa Aji Pangestu Muhlis Ahmad Abdillah

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Reviewers

1. Associate Prof. Dr. Rekha Koul Curtin University, Australia

2. Agung Sedayu, M.Sc Universitas Negeri Jakarta, Indonesia

3. Al Jupri, Ph.D Universitas Pendidikan Indonesia, Indonesia 4. Apriliana Cahya Khayrani, Ph.D Universitas Indonesia, Indonesia

5. Arif Rahman, M.Si Universitas Negeri Jakarta, Indonesia 6. Arzu Önel, Ph.D Kafkas University, Turkey

7. Daniar Setyorini, M.Pd Universitas Negeri Jakarta, Indonesia 8. Desyarti Safarini, M.Si Sampoerna University, Indonesia 9. Dewi Muliyati, M.Si, M.Sc Universitas Negeri Jakarta, Indonesia

10. Dr. Adam Malik UIN Sunan Gunung Djati Bandung, Indonesia 11. Dr. Afrizal, M.Si Universitas Negeri Jakarta, Indonesia

12. Dr. Ahmad Muhlisin, M.Pd Universitas Tidar, Indonesia

13. Dr. Amir Zaman Abdul Wali Khan, University Mardan, Pakistan 14. Dr. Ari Yuniastuti, M.Kes Universitas Negeri Semarang, Indonesia

15. Dr. Avinash Sharma National Centre for Cell Science, India 16. Dr. Bagus Sumargo Universitas Negeri Jakarta, Indonesia 17. Dr. Dalia Sukmawati, M.Si Universitas Negeri Jakarta, Indonesia 18. Dr. Diana Vivanti, M.Si Universitas Negeri Jakarta, Indonesia 19. Dr. Elisabeth Taylor Edith Cowan University, Australia 20. Dr. Ericka Darmawan, M.Pd Universitas Tidar, Indonesia

21. Dr. Erna Pasaribu Politeknik Statistika STIS, Indonesia 22. Dr. Esmar Budi, M.T Universitas Negeri Jakarta, Indonesia 23. Dr. Fera Kurniadewi, M.Si Universitas Negeri Jakarta, Indonesia 24. Dr. Firmanul Catur Wibowo Universitas Negeri Jakarta, Indonesia 25. Dr. Hanhan Dianhar, M.Si Universitas Negeri Jakarta, Indonesia 26. Dr. Hanum Isfaeni, M.Si Universitas Negeri Jakarta, Indonesia 27. Dr. Helena Margaretha Universitas Pelita Harapan, Indonesia 28. Dr. Iwan Sugihartono, M.Si Universitas Negeri Jakarta, Indonesia 29. Dr. Lukita Ambarwati Universitas Negeri Jakarta, Indonesia 30. Dr. Lyra Yulianti Universitas Andalas, Indonesia 31. Dr. Maria Pariatiowati, M.Si Universitas Negeri Jakarta, Indonesia 32. Dr. Maria Susan Anggreainy IPB University, Indonesia

33. Dr. Mieke Miarsyah, M.Si Universitas Negeri Jakarta, Indonesia 34. Dr. Nina Fitriyati, M.Kom UIN Jakarta, Indonesia

35. Dr. Paed. Nurma Yunita Indriyanti

Universitas Sebelas Maret, Indonesia 36. Dr. Paula Joyce Curtin University, Australia

37. Dr. Ratna Komala, M.Si Universitas Negeri Jakarta, Indonesia 38. Dr. Reni Indrayanti, M.Si Universitas Negeri Jakarta, Indonesia

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39. Dr. rer.nat. Bambang Heru Iswanto

Universitas Negeri Jakarta, Indonesia 40. Dr. Reza Rachmadtullah Universitas PGRI Adi Buana, Indonesia 41. Dr. Rizhal Hendi Ristanto, M.Pd Universitas Negeri Jakarta, Indonesia 42. Dr. Rully Charitas Indra

Prahmana

Universitas Ahmad Dahlan, Indonesia 43. Dr. Setia Budi, M.Sc Universitas Negeri Jakarta, Indonesia 44. dr. Subandrate, M.Biomed Universitas Sriwijaya, Indonesia 45. Dr. Titin Siswantining, DEA Universitas Indonesia

46. Dr. Tri Handayani, M.Si Universitas Negeri Jakarta, Indonesia 47. Dr. Uky Yudatama Universitas Muhammadiyah Magelang,

Indonesia

48. Dr. Umiatin Universitas Negeri Jakarta, Indonesia 49. Dr. Yudi Mahatma Universitas Negeri Jakarta, Indonesia 50. Dr. Yulia Irnidayanti, M.Si Universitas Negeri Jakarta, Indonesia 51. Drs. A. Handjoko Permana, M.Si Universitas Negeri Jakarta, Indonesia 52. Drs. Siswoyo, M.Pd Universitas Negeri Jakarta, Indonesia 53. Edith Allanas, M.Pd Universitas Negeri Jakarta, Indonesia 54. Erna Puspasari, M.Sc IPB University, Indonesia

55. Fauzan Khairi Che Harun, Ph.D UTM, Malaysia

56. Fauzi Bakri, S.Pd, M.Si Universitas Negeri Jakarta, Indonesia 57. Hadi Nasbey, Ph.D Universitas Negeri Jakarta, Indonesia 58. Hasan Bisri, M.Pd Universitas Negeri Jakarta, Indonesia 59. Jungsan Chang, Ph.D Tipei University, Taiwan

60. Laxman Luitel, M.Ed Kathmandu University, Nepal

61. Lipur Sugiyanta, Ph.D Universitas Negeri Jakarta, Indonesia 62. Maison, Ph.D Universitas Jambi, Indonesia

63. Mangaratua Simanjorang, Ph.D Universitas Negeri Medan, Indonesia 64. Neni Mariana, Ph.D Universitas Negeri Surabaya, Indonesia 65. Noor Andryan Ilsan, M.Si Taipei Univerity, Taiwan

66. Nurwati, PhD Universitas Negeri Makasar, Indonesia 67. Prof. Dr. Peter C. Taylor Murdoch University, Australia

68. Prof. Erdawati, M.Sc Universitas Negeri Jakarta, Indonesia 69. Prof. Intan Ahmad, Ph.D Institut Teknologi Bandung, Indonesia 70. Prof. Rahmah Johar Universitas Syiah Kuala, Indonesia 71. Prof. Sri Rahayu Universitas Negeri Malang, Indonesia 72. Puspitasari, S.Pd., M.Sc Universitas Negeri Jakarta, Indonesia 73. Ria Arafiah, M.Si Universitas Negeri Jakarta, Indonesia 74. Riser Fahdiran, M.Si Universitas Negeri Jakarta, Indonesia 75. Rizky Priambodo, M.Si Universitas Negeri Jakarta, Indonesia

76. Robby Zidny, S.Pd., M.Si Universitas Sultan Ageng Tirtayasa, Indonesia

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77. Roselyna Ekawati, Ph.D Universitas Negeri Surabaya, Indonesia 78. Rr. Endah Yanuarti, Ph.D. Lembaga Penjaminan Mutu Pendidikan,

Indonesia

79. Sri Rahayu, M.Biomed Universitas Negeri Jakarta, Indonesia 80. Sri Sofiati Umami, M.Biomed UIN Mataram, Indonesia

81. Sukisman Purtadi, M.Pd Universitas Negeri Yogyakarta, Indonesia 82. Dr. Teguh Budi Prayitno Universitas Negeri Jakarta, Indonesia 83. Tian Abdul Aziz, Ph.D Universitas Negeri Jakarta, Indonesia 84. Vera Maya Santi, M.Si Universitas Negeri Jakarta, Indonesia

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ACKNOWLEDGEMENTS

The 2

^nd

Science and Mathematics International Conference (SMIC 2020) proceeding could be published because of support and cooperation of various parties. We would like to thank the Dean of the Mathematics and Natural Science Faculty and all Vice Deans, the Rector and the Vice Rectors of Universitas Negeri Jakarta for the great support.

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Preventing potential misconceptions of physics students through the application of the commit and expose beliefs, confront beliefs, accommodate the concept, extend, and reflection belief prevent potential

misconceptions model

Zainuddin, Suzanna, Usman, et al.

Development of technical guidance training model of energy auditor at cement factory Indonesia to obtain professional competency certification

AIP Conference Proceedings 2331, 030019 (2021); https://doi.org/10.1063/5.0045240 How do teachers develop secondary school students’ creativity in the classroom?

AIP Conference Proceedings 2331, 030024 (2021); https://doi.org/10.1063/5.0042030 The structure of knowledge and students’ misconceptions in physics

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Preventing Potential Misconceptions of Physics Students through the Application of the Commit And ExSose Beliefs,

Confront Beliefs, Accommodate the Concept, Extend, and Reflection Belief Prevent Potential Misconceptions Model

Zainuddin

^a)

, Suzanna, Usman, and Wahab Abdi

Universitas Syiah Kuala, Kota Banda Aceh, Aceh 23111, Indonesia

a)Corresponding author: [email protected]

Abstract. Misconception is very much unwanted, because it prevents the scientific understanding of concepts. Every student who had been categorized as misconception (MC) is found to be very difficult to return to the correct understanding of concepts [1], except only by using a model of concept change. Considering the importance of understanding the accurate concepts, the purpose of this study is to prevent potential misconceptions (PMC) of Physics students through the application of the CoCoAER learning model using a scientific approach on learning material absorbing and penetrating radioactive elements. The subjects of this study are three classes of physics students’ semester VI of FKIP UNSYIAH academic year 2020/2021, with total 75 people. Using qualitative research methods related to the validity of supporting models, and the practicality of the CoCoAER model, analysis of student activities was conducted by recording the data about number of activities frequency divided by the total number of overall activity frequencies multiplied by 100%, while the quantitative methods related to the effectiveness of the model was completed by searching N-Gain. The results of the analysis showed that the CoCoAER learning model could prevent potential misconceptions (PMC) of FKIP UNSYIAH physics students with medium and high N-Gain categories ranging between (0.5 – 0.9).

INTRODUCTION

The Presidential Decree of the Republic of Indonesia (PPRI) Number 8, 2012, describes the qualification levels in Higher Education which known as Indonesian National Qualification Framework [IQF] that student must be able to;

(1)Apply field of expertise and utilize science, technology, and art in his field to solve problems and be able to adapt

to the situation at hand. This includes mastering the theoretical concepts of a particular field of knowledge in general

and the theoretical concepts of special sections in depth, and being able to formulate procedural problem solving, (2)

Plan and manage resources under their responsibilities, and comprehensively evaluate their work by utilizing science,

technology. Able to solve problems of science, technology, or art in their scientific fields through a monodisciplinary

approach, and be able to research and take strategic decisions with full accountability and responsibility for all aspects

under the responsibility of their fields of expertise, (3) Develop knowledge, technology, or art in their scientific fields

or professional practice through research, to produce innovative and proven work. Able to solve the problems of

science, technology, or art in their scientific fields through inter or multidisciplinary approaches. Able to handle

research and development which is beneficial to society and science, and able to obtain national and international

recognition. (4) Solve the problem of science, technology, and art in its scientific fields through inter, multi, and

transdisciplinary approaches.

Activity rays radiation that emitted by unstable atomic nuclei are called radioactivity, according to Henri Becquerel (1852-1908). He further found that uranium compounds contained in fluorescent salts could show certain radiation symptoms with very strong penetrating forces such as x-rays. When Piere and Marie Curie extracted uranium from Pitcblende mining material in the same laboratory as Becquerel, they found two other elements that are radioactive.

The first element is called Polonium in Poland. In 1899 Ernest Rutherford in his study of the influence of magnetic

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fields on radioactive rays, it was found that: Radioactive rays are deflected by magnetic fields which Į ray consist of positively charged particles (helium nuclei), while radioactive ray that are deflected by magnetic fields are called beta rays (ȕ). They are consists of particles with negatively charged particles (electrons). Radioactive rays which are not deflected by a magnetic field are gamma rays (Ȗ). Thus, the rays consist of neutral particles that are not electrically charged (electromagnetic waves).

The effect of magnetic fields on radioactive rays according to Ernest Rutherford: (1) Radioactive rays that are deflected by an electric field are called alpha rays (Į), (2) Radioactive rays that are deflected by magnetic fields are called beta rays (ȕ), while radioactive rays that are not deflected by a magnetic field are called gamma rays (Ȗ) (electromagnetic waves). He further explains that radioactive rays will affect the absorption and permeability of the material. The interaction of radioactive rays with material can be studied by measuring the transmission of radioactive rays through the absorbents with a certain thickness. The intensity degree of radioactive rays after going through the absorbent material (ܫ) turns out to be smaller than before passing through the absorbent material (ܫ଴). The intensity of the radioactive beam is the same as the rates of energy transport of the unified broadband from the beam. If ݀ܫ ൌ ܫ଴െ ܫ states the intensity of the radioactive rays absorbed by the absorbent material, then the fractional intensity^ௗூ_ூ

బ that is missing from the radiation beam when passing through absorbent material whose thickness is dx, turns out to be directly proportional to ݀ݔ. Therefore the expression is stated with mathematical equations:

³ ^{ ³

o

|

0 0 0

0 I

x

I dx dx dI

I

dI P

(1)

The comparison constant ߤ is called the linear attenuation coefficient and the value depends on the radioactive ray photon energy and the nature of the absorbent material:

x

I I e

I e

I

_P _P

o

₀

0

ln

(2)

ݔ indicates the thickness of the absorbent material (cm), ݁ = natural number = 2.71828 and ߤ = lineair attenuation coefficient (cm^-1). If the intensity of radioactive rays after passing through the absorbent material remains half of the intensity before passing the absorbent material (I = ½ Io), then the thickness of absorbent material x is called Half Value Layer (HVL), which means a layer of half value, which is the thickness of substance that makes the radiation intensity remained half from the initial intensity. Therefore, in this condition it produces:

) ( 0

2

0

1

_HVL

e I

I

^P

) (

2

1

_HVL

e

^P

) ( 2 ln

ln 1 e

^P⁽^HVL⁾

P HVL ) ( 6931 .

0 P HVL

P 6931 .

HVL 0

(3)

The source of the radiation ߙ ray using a radiation counter (counter) G-M tube (Geiger-Mueller) to test the penetrating power of ߙ, ߚ, and ߛ rays. If a 3 mm thick lead is inserted between aluminium and GM tube, it turns out that the reading is at the counter ߙ ൏ ߚ ൏ ߛ as shown in Fig. 1.

FIGURE 1. Absorption of radiation ߙ, ߚ, and ߛ rays by paper, aluminum and lead materials

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In Physics learning, understanding the concepts correctly and validly, according to experts, is very important, so that students can easily master the subject, and they are not trapped in the category of sustainable misconceptions (MC). Understanding the concept of natural phenomena before formal learning is called pre-concept. When the pre- concept of students is not easy to change and to return to the initial pre-concept even though the correct concept has been introduced, it is called the MC [2]. Based on the theory, meaningful learning occurs when students try to integrate new concepts with previously owned concepts and provide assessments to encourage meaningful learning [3].

Considering that learning material about radioactive rays absorption and penetrability are regarded as difficult, thus students needed to be assisted according to Vygotsky's social learning theory [4], which says that knowledge is built socially as students' social behavior develops their knowledge through discussing and debating each other individually, interpreting the results of discussions, and conducting experiments, finally obtained a mutual agreement in learning Vygotsky [4]. Active learning also shape their knowledge from personal experiences with others and the environment [5,6]. The concept is a product of a scientific process that has the same characteristics [7]. Attributes include essential features that distinguish concepts from non-concepts. The ability to distinguish is a prerequisite for learning concepts, so mastery of concepts is the ability that enables a person to do something; without mastering the concept, one cannot do much. Concept understanding is grouped into 3 categories [8], namely (1) Understanding Concept (UC), (2) Not understanding the concept (NUC), and (3) misconception (MC). The results of the pre-test about understanding concepts included in the NUC and MC categories, in this study are called potential misconception (PMC) categories.

[PMC = (CPC + MC)].

The efforts to prevent the emergence of student PMC can be done through be able to prevent the PMC, student went through diagnostic assessment to evaluate student conceptions [9]. It aimed to maps out students’ concept understanding category, and classified into the UC, NUC, and MC categories. The results of the pretest in this study, students who were categorized as MC were maintained after participating in the learning process with the CoCoAER model, while students in the NUC and MC categories were expected to have UC result in the post test after participating in the learning process with the CoCoAER model. The process of preventing PMC can be used in conceptual change models and supported by Piaget's theory of cognitive development [10]. The task of the Lecturer is to identify students' understanding of concepts to help them understand new concepts [11], through the process of Commit and Exfose Beliefs, Confront Beliefs, Accommodate the Concept, Extend the, and Reflection Beliefs.

METHOD

This study uses qualitative methods using learning support devices and non-test instruments, and quantitative methods using test instruments to detect the effect of implementing the CoCoAER model to prevent PMC, and uses qualitative methods using learning support devices and non-test instruments, and quantitative methods using test instruments, to detect the effect of the application of the CoCoAER model to prevent student PMK data obtained by qualitative methods to explain the validity of the model in terms of content validity and construction validity. The learning model is said to be valid, if the two validators (experts in the core physics and learning fields) assess the theoretical and empirical rational at least in the valid category, and the practicality of the model is seen from the Model Implementation. The CoCoAER model is said to be practical if the lecturer can implement it, the model includes: (the implementation of the syntax, social system, and reaction principle) the level of implementation at least in good category. Analysis of the feasibility of the model for the implementation of syntax in the learning phases is conducted in each meeting. Observation result from the implemetation of the model, the ability of lecturers to manage learning, activities, and student responses were analyzed by the Nieveen's formula :

% students 100

of number overall

response particular

make students of

Number Aspect

each

Respon u (4)

On the other hand, the effectiveness of the learning model in terms of preventing PMC of physics students, results from the analysis of the pretest and posttest scores that tested the average difference, significantly and N <g> of at least a medium category. So prevent student PMC by using instrument pretest and posttest, were analyzed by CRI assistance of SPSS 20. The concept understanding to prevent student PMK was carried out on the pretest and posttest scores by looking at the average N-gain score, based on Hake (1999) [12], which is:

score test pre

% - 100

score test pre

% - score post test g ! %

(5)

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TABLE 1. N-gain criteria

g !

_Criteria

7 . 0

g t

^High

7 . 0 g

0.3

^Medium

3 . 0

g d

^Low

RESULT AND DISCUSSION

Validity analysis of the CoCoAER model to prevents potential misconceptions in the physics education students of FKIP UNSYIAH on the nuclear physics material are shown in TABLE 2.

TABLE 2. Results of Content Validation of the CoCoAER Model by Experts

Scoring Aspect VAS Criteria RI(%) R

A. Content Validation

1 The purpose of using the CoCoAER model in preventing PMC FKIP UNSYIAH Physics Education students is clearly and measurably

formulated

4 VV 100 R

2 Aspects assessed (measured) by the CoCoAER model include

all theoretical foundations in developing the model 3.7 VV 86 R 3 The items stated for each aspect of the assessment in the CoCoAER model

are parallel to the measurement objectives 3.3 V 86 R

4 Words in the grading aspects of the CoCoAER model are stated clearly 3 V 100 R

Average 3.5 V 93 R

Note: VAS= Validator’s Average Score, RI= Reliability Index, VV= Very Valid, V= Valid, and R= Reliabilities.

The average content validation of the model is 3.5 which is regarded to be valid, meanwhile RI 93% is considered to be reliable. The results of this validation are supported by previous research [13]. Furthermore, the mean results of the construct validation of the CoCoAER model are as shown in TABLE 3.

TABLE 3. Construct validation results of the CoCoAER Model by Experts

Scoring Aspect VAS Criteria RI (%) R B. Construct validity

1. The CoCoAER Model Rationale 3.7 VV 86 R

2. The model supporting theory 3.8 VV 93 R

3. Development of the CoCoAER Model 3.7 VV 86 R

4. The Syntax of model 3.8 VV 90 R

5. Social System 3.8 VV 95 R

6. Principle of Reaction 3.8 VV 92 R

7. Supporting system 3.8 VV 94 R

8. Teaching and the accompanying impact 3.6 V 93 R

9. Learning Application Guidelines 3.8 VV 91 R

10. Learning environments 3.4 V 89 R

11. Evaluation 3.5 V 86 R

Average of total construct validity 3.7 VV 90 R

The average result of construct validity 3,7 is regarded to be very valid, and RI 90% is considered to be reliable.

Thus, the CoCoAER model is seen to be valid and reliable. Furthermore, the validation results of supporting learning tools for CoCoAER model to prevent the PMC of FKIP UNSYIAH students by experts obtained a mean validation of 3.5 and a reliability index of 88% that indicates to be valid category, details are shown in TABLE 4.

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TABLE 4. Results of validation analysis of supporting learning tools for CoCoAER model by expert

Scoring Aspect VAS Criteria RI (%)

A. Syllabus 3.5 V 89

B. Students learning materials (SLM) 3.4 V 86

C. Semester lesson plan (SLP) 3.6 VV 86

D. Students worksheet (SW) 3.6 VV 90

average value of supporting tools of Cocoaer Model 3.5 V 88

The validation results of the supporting tools for the CoCoAER model by the expert are fair said to be valid. On the other hand, the implementation of CoCoAER learning model, to prevent the PMC of FKIP UNSYIAH Physics Education students, had been conducted in accordance with the CoCoAER learning syntaxes.

In addition, the time allocation, that includes pre activity, core activity and post activity, supports the CoCoAER learning model in preventing the emergence of PMC among FKIP UNSYIAH Physic students. the results of the validity of instruments and model support devices In line with constructivist epistemology [3] that meaningful learning involves five elements (teacher, student, subject matter, context, and evaluation) constructively integrated with each other. The results of this validation are supported by previous research [14]. The results from I and II meeting are presented in TABLE 5 below.

TABLE . The implementation of CoCoAER learning model to prevent the PMC of FKIP UNSYIAH Physics Education students

FKIP UNSYIAH Physic students Meeting I Meeting II

Meeting I II I II I II I II

Scoring criteria AOS KI RSP KI RSP KI RSP KI

A. Pre Activity (25 minutes) Fase I 3.6 G 4 VG 3.6 G 3.9 VG

B. Core activity (100 minutes) Fase II 3.2 G 4 VG 3.2 G 3.6 G

Fase III 3.6 G 3.7 VG 3.6 G 3.6 G

Fase IV 3.3 G 4 VG 3.3 G 3.5 G

C. Post activity (25 Minutes) Fase V 3.5 G 3.9 VG 3.5 G 3.6 VG

Total RSP 3.4 G 3.9 VG VG G 3.6 VG

note: AOS (Average Observer Score), VG (Very Good) and G (Good), CI (Criteria) A. Pre Activity (25 Minutes) Fase I. Commite and Expose Beliefs

B. Core activity (100 minutes), Fase. II. Confron Biliefs (conducting experiment) (40 minutes), Fase III.

Assimilation and Accommodate the Concept (30 minutes), and Fase IV. Extende the Concept assimilation and communicating the concept (30 minutes).

C. Post activity Fase V (25 minutes) Reflect Beliefs associating.

The results from meetings I and II obtain a mean score of 3.4 and 3.9, and they are respectively classified into good and very good criteria. Meanwhile, the effectiveness of the model to prevents PMC, in nuclear physics material using a combination analysis technique of Three-tier Test answers. UC category means both answer and reason are true and confident, NUC category criteria encompasses true both answer and reason but unconfident, true reason false answer and unconfident, true answer false reason and unconfident, or false both answer and reason also unconfident, and MC category criteria consists of false answer and true reason and confident, true answer and false reason and confident, and false both answer and reason but confident [1]. The results of pretest and posttest of FKIP Unsyiah Physic Education students are presented in Fig. 2.

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FIGURE 2. Percentage (%) Distribution Category of Concept Understanding about Absorption and Radioactive Rays Penetrability of FKIP UNSYIAH Physic Education Students

Pretest results from PMC category student ranged from 94-100%, while the UC category ranged from 04%, while the posttest results of the PMC category students' scores were in the range 0-14%, and UC category students were in the range 86-96% in accordance to the adaptation of Kutluay. Furthermore, when viewed from the learning completion aspect of FKIP Unsyiah Physics Education students, initially many students did not complete the learning, however, after using CoCoAER learning model, those students were able to complete the learning. the results of the analysis of the feasibility of this model are in accordance with the recommendations before [15], that teachers should prioritize exploration of student experiences as a first step in learning; involves all aspects of conceptual understanding including interpretation, exemplifying, classifying, summarizing, referring, comparing, and explaining; and accommodate the cognitive style of students. This result is supported by an analysis of the learning achievement results such as TABLE 6.

TABLE 6. Learning Achievement Results of FKIP Unsyiah Physics Education students

Indicator Indicator Score

Max Score

Pre-test Post-test

x Completion x Completion

BA 55 2750 1,6 0,0% IC 90,3 90,0% C

PT 45 2250 3,2 0,0% IC 91,2 96,0% C

Based on the results analysis of the indicators completion, both absorption (BA) and penetrability(PT) indicators of pretest appears to be incomplete (IC), while the results of the posttest shows that the absorption indicator is 90%, and penetrability indicator is 96%. The completion(C) results are also supported by the results of the N-Gain analysis as shown in TABLE 7.

TABLE 7. N-Gain Learning outcome of FKIP Unsyiah Physics Education students

Pre-test Post-test N-gain

Score Completion Score Completion <g> Criteria

4.3 0 % Not

complete 90.7 94% Complete 0.9 High

The improvement of learning outcomes, whereas before the treatment, the pretest result that showed to be 0,0 %, while after the treatment (learning with CoCoAER model) the posttest result shows 94 %. Thus, it can be concluded that learning through CoCoAER model can prevent potential misconceptions of FKIP Unsyiah Physics Education students by learning completion result 94 % with <g> = (0,9) that can be categorized into high achievement according Hake’s formula [12]. The results of the analysis of the effectiveness and discussion of this research are supported by previous studies [8].

CONCLUSION

Based on the results of analysis and discussion, it can be concluded that learning the CoCoAER model can prevent potential misconceptions of physics students of FKIP UNSYIAH in the medium and high N-Gain category ranges.

100 90 80 70 60 50 40 30 20 10

0 1 2 3 4 5 6 7 8 9

96.0 86.0 86.0 9.0 88.0 86.0 90.0

92.0 96.0 90.0 94.0

% Postes PC

4.0 14.0 14.0 0.0 12.0 14.0 10.0

8.0 4.0 10.0 6.0

% Postes PMC

0.0 4.0 4.0 0.0 2.0 0.0 2.0

6.0 2.0 2.0 4.0

% Pretes PC

100.0 96.0 96.0 100.0 98.0 100.0 98.0

94.0 98.0 98.0 96.0

% Pretes PMC

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