Knowledge-based recommendation system for teaching computational thinking in primary level students

Julio Vera-Sanchi; Eduardo De-Rivero; Christian Condori-Mamani; Vidal Soncco-Merma; Gustavo Suero-Soto; Klinge Orlando Villalba Condori

Knowledge-based recommendation system for teaching computational thinking in primary level students

Julio Vera-Sanchi, Eduardo De-Rivero, Christian Condori-Mamani, Vidal Soncco-Merma, Gustavo Suero-Soto, Klinge Orlando Villalba Condori

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The proposal is a video game developed in Unity, which interacts with the students according to their degree of studies and, according to the STEM curriculum, it is the subject that the student has to learn and in what degree of difficulty, so Once the registration is complete, the video game will provide levels according to your academic degree so that the student interacts with the video game and use the Vector Machine Support (SVM) algorithm that enters the student data and video game data, such as its level, difficulty, time and number of movements and its result send it to the recommendation system to determine what the student should learn and provide related links or documentation, or see if he learned and at what level, verify the SVM because it is a discriminatory classifier formally defined by a hyperplane of separation, which in our case only interests us to know if the student learned or not.

Original language	English
Title of host publication	ICCE 2019 - 27th International Conference on Computers in Education, Proceedings
Editors	Maiga Chang, Hyo-Jeong So, Lung-Hsiang Wong, Ju-Ling Shih, Fu-Yun Yu, Michelle P. Banawan, Ben Chang, Weiqin Chen, Andrei D. Coronel, Swapna Gottipati, H. Ulrich Hoppe, Morris S.Y. Jong, Calvin Liao, Jon Mason, Fan Ouyang, Patcharin Panjaburee, Ma. Mercedes T. Rodrigo, Yanjie Song, Niwat Srisawasdi, Ahmed Tlili, Chengjiu Yin
Publisher	Asia-Pacific Society for Computers in Education
Pages	724-733
Number of pages	10
ISBN (Electronic)	9789869721448
State	Published - 19 Nov 2019
Externally published	Yes
Event	27th International Conference on Computers in Education, ICCE 2019 - Kenting, Taiwan, Province of China Duration: 2 Dec 2019 → 6 Dec 2019

Publication series

Name	ICCE 2019 - 27th International Conference on Computers in Education, Proceedings
Volume	2

Conference

Conference	27th International Conference on Computers in Education, ICCE 2019
Country/Territory	Taiwan, Province of China
City	Kenting
Period	2/12/19 → 6/12/19

Bibliographical note

Funding Information:
The term STEM is the acronym for the English terms Science, Technology, Engineering and Mathematics. The term was coined by the National Science Foundation (NSF) in the 1990s. [8] the term STEM, only dried, only serves to group the 4 major areas of knowledge in which scientists and engineers work. The concept of “STEM Education” (STEM Education) has been developed as a new way of teaching Science, Mathematics and Technology together (in general, not only computer science) with two distinct characteristics: [11]  Teaching-learning of Science, Technology, Engineering and Mathematics in an integrated manner rather than as compartmentalized areas of knowledge. Integrated instruction means any program in which there is an explicit assimilation of concepts from two or more disciplines.  With an engineering approach to the development of theoretical knowledge for its subsequent practical application, always focused on solving technological problems. [14]

Publisher Copyright:
© ICCE 2019 - 27th International Conference on Computers in Education, Proceedings. All rights reserved.

Keywords

Computational thinking
Machine learning
Recommendation system

Cite this

Vera-Sanchi, J., De-Rivero, E., Condori-Mamani, C., Soncco-Merma, V., Suero-Soto, G., & Villalba Condori, K. O. (2019). Knowledge-based recommendation system for teaching computational thinking in primary level students. In M. Chang, H-J. So, L-H. Wong, J-L. Shih, F-Y. Yu, M. P. Banawan, B. Chang, W. Chen, A. D. Coronel, S. Gottipati, H. U. Hoppe, M. S. Y. Jong, C. Liao, J. Mason, F. Ouyang, P. Panjaburee, M. M. T. Rodrigo, Y. Song, N. Srisawasdi, A. Tlili, ... C. Yin (Eds.), ICCE 2019 - 27th International Conference on Computers in Education, Proceedings (pp. 724-733). (ICCE 2019 - 27th International Conference on Computers in Education, Proceedings; Vol. 2). Asia-Pacific Society for Computers in Education.

Vera-Sanchi, Julio ; De-Rivero, Eduardo ; Condori-Mamani, Christian et al. / Knowledge-based recommendation system for teaching computational thinking in primary level students. ICCE 2019 - 27th International Conference on Computers in Education, Proceedings. editor / Maiga Chang ; Hyo-Jeong So ; Lung-Hsiang Wong ; Ju-Ling Shih ; Fu-Yun Yu ; Michelle P. Banawan ; Ben Chang ; Weiqin Chen ; Andrei D. Coronel ; Swapna Gottipati ; H. Ulrich Hoppe ; Morris S.Y. Jong ; Calvin Liao ; Jon Mason ; Fan Ouyang ; Patcharin Panjaburee ; Ma. Mercedes T. Rodrigo ; Yanjie Song ; Niwat Srisawasdi ; Ahmed Tlili ; Chengjiu Yin. Asia-Pacific Society for Computers in Education, 2019. pp. 724-733 (ICCE 2019 - 27th International Conference on Computers in Education, Proceedings).

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title = "Knowledge-based recommendation system for teaching computational thinking in primary level students",

abstract = "The proposal is a video game developed in Unity, which interacts with the students according to their degree of studies and, according to the STEM curriculum, it is the subject that the student has to learn and in what degree of difficulty, so Once the registration is complete, the video game will provide levels according to your academic degree so that the student interacts with the video game and use the Vector Machine Support (SVM) algorithm that enters the student data and video game data, such as its level, difficulty, time and number of movements and its result send it to the recommendation system to determine what the student should learn and provide related links or documentation, or see if he learned and at what level, verify the SVM because it is a discriminatory classifier formally defined by a hyperplane of separation, which in our case only interests us to know if the student learned or not.",

keywords = "Computational thinking, Machine learning, Recommendation system",

author = "Julio Vera-Sanchi and Eduardo De-Rivero and Christian Condori-Mamani and Vidal Soncco-Merma and Gustavo Suero-Soto and {Villalba Condori}, {Klinge Orlando}",

note = "Publisher Copyright: {\textcopyright} ICCE 2019 - 27th International Conference on Computers in Education, Proceedings. All rights reserved.; null ; Conference date: 02-12-2019 Through 06-12-2019",

year = "2019",

month = nov,

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language = "Ingl{\'e}s",

series = "ICCE 2019 - 27th International Conference on Computers in Education, Proceedings",

publisher = "Asia-Pacific Society for Computers in Education",

pages = "724--733",

editor = "Maiga Chang and Hyo-Jeong So and Lung-Hsiang Wong and Ju-Ling Shih and Fu-Yun Yu and Banawan, {Michelle P.} and Ben Chang and Weiqin Chen and Coronel, {Andrei D.} and Swapna Gottipati and Hoppe, {H. Ulrich} and Jong, {Morris S.Y.} and Calvin Liao and Jon Mason and Fan Ouyang and Patcharin Panjaburee and Rodrigo, {Ma. Mercedes T.} and Yanjie Song and Niwat Srisawasdi and Ahmed Tlili and Chengjiu Yin",

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Vera-Sanchi, J, De-Rivero, E, Condori-Mamani, C, Soncco-Merma, V, Suero-Soto, G & Villalba Condori, KO 2019, Knowledge-based recommendation system for teaching computational thinking in primary level students. in M Chang, H-J So, L-H Wong, J-L Shih, F-Y Yu, MP Banawan, B Chang, W Chen, AD Coronel, S Gottipati, HU Hoppe, MSY Jong, C Liao, J Mason, F Ouyang, P Panjaburee, MMT Rodrigo, Y Song, N Srisawasdi, A Tlili & C Yin (eds), ICCE 2019 - 27th International Conference on Computers in Education, Proceedings. ICCE 2019 - 27th International Conference on Computers in Education, Proceedings, vol. 2, Asia-Pacific Society for Computers in Education, pp. 724-733, 27th International Conference on Computers in Education, ICCE 2019, Kenting, Taiwan, Province of China, 2/12/19.

Knowledge-based recommendation system for teaching computational thinking in primary level students. / Vera-Sanchi, Julio; De-Rivero, Eduardo; Condori-Mamani, Christian et al.

ICCE 2019 - 27th International Conference on Computers in Education, Proceedings. ed. / Maiga Chang; Hyo-Jeong So; Lung-Hsiang Wong; Ju-Ling Shih; Fu-Yun Yu; Michelle P. Banawan; Ben Chang; Weiqin Chen; Andrei D. Coronel; Swapna Gottipati; H. Ulrich Hoppe; Morris S.Y. Jong; Calvin Liao; Jon Mason; Fan Ouyang; Patcharin Panjaburee; Ma. Mercedes T. Rodrigo; Yanjie Song; Niwat Srisawasdi; Ahmed Tlili; Chengjiu Yin. Asia-Pacific Society for Computers in Education, 2019. p. 724-733 (ICCE 2019 - 27th International Conference on Computers in Education, Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Knowledge-based recommendation system for teaching computational thinking in primary level students

AU - Vera-Sanchi, Julio

AU - De-Rivero, Eduardo

AU - Condori-Mamani, Christian

AU - Soncco-Merma, Vidal

AU - Suero-Soto, Gustavo

AU - Villalba Condori, Klinge Orlando

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AB - The proposal is a video game developed in Unity, which interacts with the students according to their degree of studies and, according to the STEM curriculum, it is the subject that the student has to learn and in what degree of difficulty, so Once the registration is complete, the video game will provide levels according to your academic degree so that the student interacts with the video game and use the Vector Machine Support (SVM) algorithm that enters the student data and video game data, such as its level, difficulty, time and number of movements and its result send it to the recommendation system to determine what the student should learn and provide related links or documentation, or see if he learned and at what level, verify the SVM because it is a discriminatory classifier formally defined by a hyperplane of separation, which in our case only interests us to know if the student learned or not.

KW - Computational thinking

KW - Machine learning

KW - Recommendation system

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M3 - Contribución a la conferencia

AN - SCOPUS:85077717442

T3 - ICCE 2019 - 27th International Conference on Computers in Education, Proceedings

SP - 724

EP - 733

BT - ICCE 2019 - 27th International Conference on Computers in Education, Proceedings

A2 - Chang, Maiga

A2 - So, Hyo-Jeong

A2 - Wong, Lung-Hsiang

A2 - Shih, Ju-Ling

A2 - Yu, Fu-Yun

A2 - Banawan, Michelle P.

A2 - Chang, Ben

A2 - Chen, Weiqin

A2 - Coronel, Andrei D.

A2 - Gottipati, Swapna

A2 - Hoppe, H. Ulrich

A2 - Jong, Morris S.Y.

A2 - Liao, Calvin

A2 - Mason, Jon

A2 - Ouyang, Fan

A2 - Panjaburee, Patcharin

A2 - Rodrigo, Ma. Mercedes T.

A2 - Song, Yanjie

A2 - Srisawasdi, Niwat

A2 - Tlili, Ahmed

A2 - Yin, Chengjiu

PB - Asia-Pacific Society for Computers in Education

Y2 - 2 December 2019 through 6 December 2019

ER -

Vera-Sanchi J, De-Rivero E, Condori-Mamani C, Soncco-Merma V, Suero-Soto G, Villalba Condori KO. Knowledge-based recommendation system for teaching computational thinking in primary level students. In Chang M, So H-J, Wong L-H, Shih J-L, Yu F-Y, Banawan MP, Chang B, Chen W, Coronel AD, Gottipati S, Hoppe HU, Jong MSY, Liao C, Mason J, Ouyang F, Panjaburee P, Rodrigo MMT, Song Y, Srisawasdi N, Tlili A, Yin C, editors, ICCE 2019 - 27th International Conference on Computers in Education, Proceedings. Asia-Pacific Society for Computers in Education. 2019. p. 724-733. (ICCE 2019 - 27th International Conference on Computers in Education, Proceedings).

Knowledge-based recommendation system for teaching computational thinking in primary level students

Abstract

Publication series

Conference

Bibliographical note

Keywords

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