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Erschienen in: Wireless Personal Communications 3/2021

01.02.2021

Personalized Learning in a Virtual Learning Environment Using Modification of Objective Distance

verfasst von: Sataworn Chaichumpa, Santichai Wicha, Punnarumol Temdee

Erschienen in: Wireless Personal Communications | Ausgabe 3/2021

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Abstract

For a virtual learning environment (VLE), assessing student’s competency is important because the proper feedback for students to achieve their goals is re-quired differently. Therefore, it is necessary to have an appropriate measurement for evaluating student's competency so that personalized support can be provided. This study proposes the attribute to determine an individual's competency by modifying the Objective Distance, which is the distance from the current status of a student's competency towards a satisfied competency. In this study, the Objective Distance is modified based on the assumption that the entire course's achievement is obtained from different learning objects' priorities. The proposed attribute was studied to determine to what extent it would represent diverse learners' competencies. The study was done with 55 students in an online learning course through the VLE. The students could choose learning objects freely. The learning enhancement was done through the analysis of pre-test and post-test for all learning objects. The classification results with K-Nearest Neighbor and Artificial Neural Network show that both the original and the modified Objective Distance are effectively used to assess the individual's competency. Overall, the modified Objective Distance performs better than the original one.
Literatur
1.
Zurück zum Zitat Cristea, A. I., & De Mooij, A. (2003). Adaptive course authoring: My online teacher. In 10th International Conference on Telecommunications, 2003. ICT 2003 (Vol . 2, pp. 1762–1769). IEEE . Cristea, A. I., & De Mooij, A. (2003). Adaptive course authoring: My online teacher. In 10th International Conference on Telecommunications, 2003. ICT 2003 (Vol . 2, pp. 1762–1769). IEEE .
2.
Zurück zum Zitat Hong, C. M., Chen, C. M., Chang, M. H., & Chen, S. C. (2007). Intelligent web-based tutoring system with personalized learning path guidance. In Seventh IEEE international conference on advanced learning technologies, 2007. ICALT 2007 (pp. 512–516). IEEE. Hong, C. M., Chen, C. M., Chang, M. H., & Chen, S. C. (2007). Intelligent web-based tutoring system with personalized learning path guidance. In Seventh IEEE international conference on advanced learning technologies, 2007. ICALT 2007 (pp. 512–516). IEEE.
3.
Zurück zum Zitat Huang, M., Huang, H., & Chen, M. (2007). Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications, 33(3), 551–564. CrossRef Huang, M., Huang, H., & Chen, M. (2007). Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications, 33(3), 551–564. CrossRef
4.
Zurück zum Zitat Bai, S. M., & Chen, S. M. (2008). Automatically constructing concept maps based on fuzzy rules for adapting learning systems. Expert Systems with Applications, 35(1–2), 41–49. CrossRef Bai, S. M., & Chen, S. M. (2008). Automatically constructing concept maps based on fuzzy rules for adapting learning systems. Expert Systems with Applications, 35(1–2), 41–49. CrossRef
5.
Zurück zum Zitat Chen, C. M., & Chung, C. J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624–645. MathSciNetCrossRef Chen, C. M., & Chung, C. J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624–645. MathSciNetCrossRef
6.
Zurück zum Zitat Bhaskar, M., Das, M. M., Chithralekha, T., & Sivasatya, S. (2010). Genetic algorithm based adaptive learning scheme generation for context aware e-learning. International Journal on Computer Science and Engineering, 2(4), 1271–1279. Bhaskar, M., Das, M. M., Chithralekha, T., & Sivasatya, S. (2010). Genetic algorithm based adaptive learning scheme generation for context aware e-learning. International Journal on Computer Science and Engineering, 2(4), 1271–1279.
7.
Zurück zum Zitat Chu, C. P., Chang, Y. C., & Tsai, C. C. (2011). PC 2 PSO: personalized e-course composition based on Particle Swarm Optimization. Applied Intelligence, 34(1), 141–154. CrossRef Chu, C. P., Chang, Y. C., & Tsai, C. C. (2011). PC 2 PSO: personalized e-course composition based on Particle Swarm Optimization. Applied Intelligence, 34(1), 141–154. CrossRef
8.
Zurück zum Zitat Alshalabi, I. A., Hamada, S., & Elleithy, K. (2015). Automated adaptive learning using smart shortest path algorithm for course units. In Systems, applications and technology conference ( LISAT) , 2015 IEEE Long Island (pp. 1–5). IEEE. Alshalabi, I. A., Hamada, S., & Elleithy, K. (2015). Automated adaptive learning using smart shortest path algorithm for course units. In Systems, applications and technology conference ( LISAT) , 2015 IEEE Long Island (pp. 1–5). IEEE.
9.
Zurück zum Zitat DwiC, A., & Basuki, A. (2012). Personalized learning path of a web-based learning system. International Journal of Computer Applications, 53(7), 17–22. CrossRef DwiC, A., & Basuki, A. (2012). Personalized learning path of a web-based learning system. International Journal of Computer Applications, 53(7), 17–22. CrossRef
10.
Zurück zum Zitat Brusilovsky, P., & Henze, N. (2007). Open corpus adaptive educational hypermedia. In The adaptive web (pp. 671–696). Berlin: Springer. Brusilovsky, P., & Henze, N. (2007). Open corpus adaptive educational hypermedia. In The adaptive web (pp. 671–696). Berlin: Springer.
11.
Zurück zum Zitat Hwang, G. J., Sung, H. Y., Hung, C. M., & Huang, I. (2013). A learning style perspective to investigate the necessity of developing adaptive learning systems. Educational Technology & Society, 16(2), 188–197. Hwang, G. J., Sung, H. Y., Hung, C. M., & Huang, I. (2013). A learning style perspective to investigate the necessity of developing adaptive learning systems. Educational Technology & Society, 16(2), 188–197.
12.
Zurück zum Zitat Chenoweth, T., Corral, K., & Scott, K. (2016). Automated feedback as a convergence tool. Journal of Information Systems Education, 27(1), 7. Chenoweth, T., Corral, K., & Scott, K. (2016). Automated feedback as a convergence tool. Journal of Information Systems Education, 27(1), 7.
13.
Zurück zum Zitat Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26. Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.
14.
Zurück zum Zitat Lee, I. (2008). Student reactions to teacher feedback in two Hong Kong secondary classrooms. Journal of Second Language Writing, 17(3), 144–164. CrossRef Lee, I. (2008). Student reactions to teacher feedback in two Hong Kong secondary classrooms. Journal of Second Language Writing, 17(3), 144–164. CrossRef
15.
Zurück zum Zitat Robinson, C. C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84(2), 101–109. CrossRef Robinson, C. C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84(2), 101–109. CrossRef
16.
Zurück zum Zitat Malgorzata, S. Z., & Waalen, J. K. (2002). The effect of individual learning styles on student outcomes in technology-enabled education. Global Journal of Engineering Education, 6(1), 35–43. Malgorzata, S. Z., & Waalen, J. K. (2002). The effect of individual learning styles on student outcomes in technology-enabled education. Global Journal of Engineering Education, 6(1), 35–43.
17.
Zurück zum Zitat Tomlinson, C. A. (2014). The differentiated classroom: Responding to the needs of all learners. Alexandria: ASCD. Tomlinson, C. A. (2014). The differentiated classroom: Responding to the needs of all learners. Alexandria: ASCD.
18.
Zurück zum Zitat Vargas-Vera, M., & Lytras, M. (2008). Personalized learning using ontologies and semantic web technologies. In World summit on knowledge society (pp. 177–186). Berlin: Springer. Vargas-Vera, M., & Lytras, M. (2008). Personalized learning using ontologies and semantic web technologies. In World summit on knowledge society (pp. 177–186). Berlin: Springer.
19.
Zurück zum Zitat Brusilovsky, P., & Vassileva, J. (2003). Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Life Long Learning, 13(1–2), 75–94. CrossRef Brusilovsky, P., & Vassileva, J. (2003). Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Life Long Learning, 13(1–2), 75–94. CrossRef
20.
Zurück zum Zitat Chaichumpa, S., Wicha, S. & Temdee, P. (2016). Personalized learning in a virtual learning environment using classification of objective distance. In Global wireless summit. November 27–30, 2016, Aarhus University, Aarhus, Denmark (pp. 411–414). Chaichumpa, S., Wicha, S. & Temdee, P. (2016). Personalized learning in a virtual learning environment using classification of objective distance. In Global wireless summit. November 27–30, 2016, Aarhus University, Aarhus, Denmark (pp. 411–414).
21.
Zurück zum Zitat Chaichumpa, S., & Temdee, P. (2018). Assessment of student competency for personalized online learning using objective distance. International Journal of Innovation and Learning, 23(1), 19–36. CrossRef Chaichumpa, S., & Temdee, P. (2018). Assessment of student competency for personalized online learning using objective distance. International Journal of Innovation and Learning, 23(1), 19–36. CrossRef
22.
Zurück zum Zitat Chaichumpa, S., & Temdee, P. (2018). Multi-agents platform for mobile learning using objective distance based personalisation method. International Journal of Mobile Learning and Organization, 12(3), 293–310. CrossRef Chaichumpa, S., & Temdee, P. (2018). Multi-agents platform for mobile learning using objective distance based personalisation method. International Journal of Mobile Learning and Organization, 12(3), 293–310. CrossRef
23.
Zurück zum Zitat Kumar, A., Pakala, R., Ragade, R. K., & Wong, J. P. (1998). The virtual learning environment system. In Frontiers in education conference, 1998. FIE'98. 28th annual (Vol. 2, pp. 711–716). IEEE. Kumar, A., Pakala, R., Ragade, R. K., & Wong, J. P. (1998). The virtual learning environment system. In Frontiers in education conference, 1998. FIE'98. 28th annual (Vol. 2, pp. 711–716). IEEE.
24.
Zurück zum Zitat Clements, M., & Smalley, M. (2000). Opportunities to enhance the student learning experience using a virtual learning environment. In P. Davies, S. Hodkinson, et al. (Eds.), Innovative approaches to learning and teaching in economics and business higher education, pp. 77–98. Clements, M., & Smalley, M. (2000). Opportunities to enhance the student learning experience using a virtual learning environment. In P. Davies, S. Hodkinson, et al. (Eds.), Innovative approaches to learning and teaching in economics and business higher education, pp. 77–98.
25.
Zurück zum Zitat Tucker, A. (2003). A model curriculum for K-12 computer science: Final report of the ACM K-12 task force curriculum committee. Tucker, A. (2003). A model curriculum for K-12 computer science: Final report of the ACM K-12 task force curriculum committee.
26.
Zurück zum Zitat Brusilovsky, P., Eklund, J., & Schwarz, E. (1998). Web-based education for all: a tool for development adaptive courseware. Computer networks and ISDN systems, 30(1–7), 291–300. CrossRef Brusilovsky, P., Eklund, J., & Schwarz, E. (1998). Web-based education for all: a tool for development adaptive courseware. Computer networks and ISDN systems, 30(1–7), 291–300. CrossRef
27.
Zurück zum Zitat Murray, T., Blessing, S., & Ainsworth, S. (Eds.). (2003). Authoring tools for advanced technology learning environments: Toward cost-effective adaptive, interactive and intelligent educational software. Berlin: Springer. Murray, T., Blessing, S., & Ainsworth, S. (Eds.). (2003). Authoring tools for advanced technology learning environments: Toward cost-effective adaptive, interactive and intelligent educational software. Berlin: Springer.
28.
Zurück zum Zitat Sangineto, E. (2008). An adaptive e-learning platform for personalized course generation. Architecture Solutions for E-Learning Systems, 262–281. Sangineto, E. (2008). An adaptive e-learning platform for personalized course generation. Architecture Solutions for E-Learning Systems, 262–281.
29.
Zurück zum Zitat Gauch, S., Speretta, M., Chandramouli, A., & Micarelli, A. (2007). User profiles for personalized information access. In The adaptive web (pp. 54–89). Springer, Berlin Gauch, S., Speretta, M., Chandramouli, A., & Micarelli, A. (2007). User profiles for personalized information access. In The adaptive web (pp. 54–89). Springer, Berlin
30.
Zurück zum Zitat Yarandi, M., Jahankhani, H., & Tawil, A. R. H. (2013). A personalized adaptive e-learning approach based on semantic web technology. Webology, 10(2), 1. Yarandi, M., Jahankhani, H., & Tawil, A. R. H. (2013). A personalized adaptive e-learning approach based on semantic web technology. Webology, 10(2), 1.
31.
Zurück zum Zitat Chen, C. M., Lee, H. M., & Chen, Y. H. (2005). Personalized e-learning system using item response theory. Computers & Education, 44(3), 237–255. CrossRef Chen, C. M., Lee, H. M., & Chen, Y. H. (2005). Personalized e-learning system using item response theory. Computers & Education, 44(3), 237–255. CrossRef
32.
Zurück zum Zitat Brown, G. A., Bull, J., & Pendlebury, M. (2013). Assessing student learning in higher education. Abingdon: Routledge. CrossRef Brown, G. A., Bull, J., & Pendlebury, M. (2013). Assessing student learning in higher education. Abingdon: Routledge. CrossRef
33.
Zurück zum Zitat Boud, D., & Brew, A. (1995). Developing a typology for learner self assessment practices. Research and development in Higher Education, 18(1), 130–135. Boud, D., & Brew, A. (1995). Developing a typology for learner self assessment practices. Research and development in Higher Education, 18(1), 130–135.
34.
Zurück zum Zitat Kovacic, Z. & Green, J. S. (2010). Automated assessment of advanced office skills. The open polytechnic of New Zealand, Private Bag 31914, Lower Hutt, New Zealand. Kovacic, Z. & Green, J. S. (2010). Automated assessment of advanced office skills. The open polytechnic of New Zealand, Private Bag 31914, Lower Hutt, New Zealand.
35.
Zurück zum Zitat Baradwaj, B. K. and Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications ( IJACSA) , 2(6). Baradwaj, B. K. and Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications ( IJACSA) , 2(6).
36.
Zurück zum Zitat Liu, J. (2013). The assessment agent system: Design, development, and evaluation. Educational Technology Research and Development, 61(2), 197–215. CrossRef Liu, J. (2013). The assessment agent system: Design, development, and evaluation. Educational Technology Research and Development, 61(2), 197–215. CrossRef
37.
Zurück zum Zitat Temdee, P. (2014). Ubiquitous learning environment: Smart learning platform with multi-agent architecture. Wireless Personal Communications, 76(3), 627–641. CrossRef Temdee, P. (2014). Ubiquitous learning environment: Smart learning platform with multi-agent architecture. Wireless Personal Communications, 76(3), 627–641. CrossRef
38.
Zurück zum Zitat Hardgrave, B. C., Wilson, R. L., & Walstrom, K. A. (1994). Predicting graduate student success: A comparison of neural networks and traditional techniques. Computers & Operations Research, 21(3), 249–263. CrossRef Hardgrave, B. C., Wilson, R. L., & Walstrom, K. A. (1994). Predicting graduate student success: A comparison of neural networks and traditional techniques. Computers & Operations Research, 21(3), 249–263. CrossRef
39.
Zurück zum Zitat Chen, L. H. (2011). Enhancement of student learning performance using personalized diagnosis and remedial learning system. Computers & Education, 56(1), 289–299. CrossRef Chen, L. H. (2011). Enhancement of student learning performance using personalized diagnosis and remedial learning system. Computers & Education, 56(1), 289–299. CrossRef
40.
Zurück zum Zitat Shahiri, A. M., & Husain, W. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414–422. CrossRef Shahiri, A. M., & Husain, W. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414–422. CrossRef
41.
Zurück zum Zitat Mayilvaganan, M., & Kalpanadevi, D. (2014). Comparison of classification techniques for predicting the performance of students academic environment. In 2014 international conference on communication and network technologies ( ICCNT) (pp. 113–118). IEEE. Mayilvaganan, M., & Kalpanadevi, D. (2014). Comparison of classification techniques for predicting the performance of students academic environment. In 2014 international conference on communication and network technologies ( ICCNT) (pp. 113–118). IEEE.
42.
Zurück zum Zitat Tuparova, D., & Tuparov, G. (2010). Automated real-live performance-based assessment of ICT skills. Procedia-Social and Behavioral Sciences, 2(2), 4747–4751. CrossRef Tuparova, D., & Tuparov, G. (2010). Automated real-live performance-based assessment of ICT skills. Procedia-Social and Behavioral Sciences, 2(2), 4747–4751. CrossRef
43.
Zurück zum Zitat Ballantine, J. A., Larres, P. M., & Oyelere, P. (2007). Computer usage and the validity of self-assessed computer competence among first-year business students. Computers & Education, 49(4), 976–990. CrossRef Ballantine, J. A., Larres, P. M., & Oyelere, P. (2007). Computer usage and the validity of self-assessed computer competence among first-year business students. Computers & Education, 49(4), 976–990. CrossRef
44.
Zurück zum Zitat Temdee, P., & Intayoad, W. (2014). Discovering and analyzing learning pattern on web based learning using social network analysis. In Signal and information processing association annual summit and conference (APSIPA), 2014 Asia-Pacific (pp. 1–4). IEEE. Temdee, P., & Intayoad, W. (2014). Discovering and analyzing learning pattern on web based learning using social network analysis. In Signal and information processing association annual summit and conference (APSIPA), 2014 Asia-Pacific (pp. 1–4). IEEE.
45.
Zurück zum Zitat MacLeod, J. E., Luk, A., & Titterington, D. M. (1987). A re-examination of the distance-weighted k-nearest neighbor classification rule. IEEE Transactions on Systems, Man, and Cybernetics, 17(4), 689–696. CrossRef MacLeod, J. E., Luk, A., & Titterington, D. M. (1987). A re-examination of the distance-weighted k-nearest neighbor classification rule. IEEE Transactions on Systems, Man, and Cybernetics, 17(4), 689–696. CrossRef
46.
Zurück zum Zitat Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the Association for Information Science and Technology, 60(2), 372–380. Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the Association for Information Science and Technology, 60(2), 372–380.
47.
Zurück zum Zitat Dasarasthy, B. V. (1991). Nearest neighbor pattern classification techniques. IEEE Computer Society Press. Dasarasthy, B. V. (1991). Nearest neighbor pattern classification techniques. IEEE Computer Society Press.
48.
Zurück zum Zitat Shakhnarovich, G., Darrell, T., & Indyk, P. (2008). Nearest-neighbor methods in learning and vision. IEEE Transactions on Neural Networks, 19(2), 377. CrossRef Shakhnarovich, G., Darrell, T., & Indyk, P. (2008). Nearest-neighbor methods in learning and vision. IEEE Transactions on Neural Networks, 19(2), 377. CrossRef
49.
Zurück zum Zitat Tanner, T., & Toivonen, H. (2010). Predicting and preventing student failure–using the k-nearest neighbour method to predict student performance in an online course environment. International Journal of Learning Technology, 5(4), 356–377. CrossRef Tanner, T., & Toivonen, H. (2010). Predicting and preventing student failure–using the k-nearest neighbour method to predict student performance in an online course environment. International Journal of Learning Technology, 5(4), 356–377. CrossRef
50.
Zurück zum Zitat Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003, November). Predicting student performance: an application of data mining methods with an educational web-based system. In Frontiers in education, 2003. FIE 2003 33rd annual (Vol. 1, pp. T2A-13). IEEE. Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003, November). Predicting student performance: an application of data mining methods with an educational web-based system. In Frontiers in education, 2003. FIE 2003 33rd annual (Vol. 1, pp. T2A-13). IEEE.
51.
Zurück zum Zitat Kubat, M. (1999). Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994. The Knowledge Engineering Review, 13(4), 409–412. CrossRef Kubat, M. (1999). Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994. The Knowledge Engineering Review, 13(4), 409–412. CrossRef
52.
Zurück zum Zitat Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and E-learning, 17(1), 118–133. CrossRef Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and E-learning, 17(1), 118–133. CrossRef
53.
Zurück zum Zitat Zhang, G. P. (2000). Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30(4), 451–462. CrossRef Zhang, G. P. (2000). Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30(4), 451–462. CrossRef
54.
Zurück zum Zitat Paliwal, M., & Kumar, U. A. (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4), 7865–7872. CrossRef Paliwal, M., & Kumar, U. A. (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4), 7865–7872. CrossRef
55.
Zurück zum Zitat Rashid, T. A., & Aziz, N. K. (2016). Student academic performance using artificial intelligence. ZANCO Journal of Pure and Applied Sciences, 28(2). Rashid, T. A., & Aziz, N. K. (2016). Student academic performance using artificial intelligence. ZANCO Journal of Pure and Applied Sciences, 28(2).
56.
Zurück zum Zitat Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting students’ academic performance using artificial neural network: A case study of an engineering course. The Pacific Journal of Science and Technology, 9(1), 72–79. Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting students’ academic performance using artificial neural network: A case study of an engineering course. The Pacific Journal of Science and Technology, 9(1), 72–79.
57.
58.
Zurück zum Zitat Patankar, B., & Chavda, V. (2014). A comparative study of decision tree, Naive Bayesian and KNN classifier in data mining. International Journal of Advanced Research in Computer Science and Software Engineering, 4(12), 776–779. Patankar, B., & Chavda, V. (2014). A comparative study of decision tree, Naive Bayesian and KNN classifier in data mining. International Journal of Advanced Research in Computer Science and Software Engineering, 4(12), 776–779.
59.
Zurück zum Zitat Brame, M. (2007). Principles of data mining (pp. 173–176). Berlin: Springer. Brame, M. (2007). Principles of data mining (pp. 173–176). Berlin: Springer.
Metadaten
Titel
Personalized Learning in a Virtual Learning Environment Using Modification of Objective Distance
verfasst von
Sataworn Chaichumpa
Santichai Wicha
Punnarumol Temdee
Publikationsdatum
01.02.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-08126-7

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