Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem
Abstract
:1. Introduction
2. Smart E-Learning Theory
3. Student Level Test Concept
4. Computer Simulation for Learning by Level
Algorithm 1 |
[System] Name = FUZZY-LEVEL-TEST Type = ’mamdani’ Version = 2.0 NumInputs = 3 NumOutputs = 1 NumRules = 9 AndMethod = ‘min’ OrMethod = ‘max’ ImpMethod = ‘min’ AggMethod = ‘max’ DefuzzMethod = ‘centroid’ [Input1] Name = ‘SCORE’ Range = [0 100] NumMFs = 3 MF1 = ‘BAD’: ‘gaussmf’, [15 0] MF2 = ‘AVERAGE’: ‘gaussmf’, [15 50] MF3 = ‘Exellent’: ‘gaussmf’, [15 100] [Input2] Name = ‘Difficulty’ Range = [0 100] NumMFs = 3 MF1 = ‘SMALL’: ‘trapmf’, [0 0 10 37.17] MF2 = ‘BIG’: ‘trapmf’, [58.33 90 100 100] MF3 = ‘MIDDLE’: ‘trimf’, [20.64 51.14 70.56] [Input3] Name = ‘Wrong-ANSWER’ Range = [0 100] NumMFs = 3 MF1 = ‘small’: ‘trapmf’, [−36 −8 12 36] MF2 = ‘medium’: ‘gaussmf’, [12.74 50] MF3 = ‘big’: ‘trapmf’, [64 85 104 136] [Output1] Name = ‘PASS’ Range = [0 118] NumMFs = 3 MF1 = ‘LOW’: ‘trapmf’, [−143.9 1.298 8.26 54.16] MF2 = ‘Average’: ‘gaussmf’, [10.01 59.15] MF3 = ‘HIGH’: ‘trapmf’, [64.78 109.3 127.5 187.7] [Rules] 3 0 0, 3 (1): 2 2 0 0, 2 (1): 2 3 1 3, 3 (1): 1 3 0 3, 3 (1): 2 2 0 2, 2 (1): 2 3 1 0, 3 (1): 2 1 2 0, 1 (1): 2 0 0 1, 1 (1): 2 0 0 3, 3 (1): 2 |
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Sarwar, B.; Bajwa, I.S.; Jamil, N.; Ramzan, S.; Sarwar, N. An intelligent fire warning application using IoT and an adaptive neuro-fuzzy inference system. Sensors 2019, 19, 3150. [Google Scholar] [CrossRef] [Green Version]
- Hadizadeh, M.; Farzanegan, A.; Noaparast, M. A plant-scale validated MATLAB-based fuzzy expert system to control SAG mill circuits. J. Process Control 2018, 70, 1–11. [Google Scholar] [CrossRef]
- Lee, J.Y.; Jung, K.D.; Shrestha, B.; Lee, J.; Cho, S. Energy efficiency improvement of the of a cluster head selection for wireless sensor networks. Int. J. Smart Home 2014, 8, 9–18. [Google Scholar] [CrossRef]
- Joshi, G.P.; Jha, S.; Cho, S.; Seo, C.; Son, L.H.; Thong, P.H. Influence of multimedia and seating location in academic engagement and grade performance of students. Comput. Appl. Eng. Educ. 2020, 28, 268–281. [Google Scholar] [CrossRef]
- Sharma, J.; Matheussen, B.V.; Glimsdal, S.; Granmo, O.C. Hydropower Optimization Using Split-Window, Meta-Heuristic and Genetic Algorithms. In Proceedings of the 18th IEEE International Conference on Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 16–19 December 2019; pp. 882–888. [Google Scholar]
- Solak, S.; Ucar, M.H.; Albadwieh, M. Computer-based evaluation to assess students’ learning for the multiple-choice question–based exams: CBE-MCQs software tool. Comput. Appl. Eng. Educ. 2020, 28, 1406–1420. [Google Scholar] [CrossRef]
- Agarwal, D.; Chen, B.C.; Elango, P. Fast online learning through offline initialization for time-sensitive recommendation. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 25–28 July 2010; pp. 703–712. [Google Scholar]
- Chiang, P.H.; Dey, S. Offline and online learning techniques for personalized blood pressure prediction and health behavior recommendations. IEEE Access 2019, 7, 130854–130864. [Google Scholar] [CrossRef]
- Hagras, H.; Callaghan, V.; Colley, M.; Clarke, G. A hierarchical fuzzy–genetic multi-agent architecture for intelligent buildings online learning, adaptation and control. Inf. Sci. 2003, 150, 33–57. [Google Scholar] [CrossRef]
- Shepherd, T.; Prince, S.J.; Alexander, D.C. Interactive lesion segmentation with shape priors from offline and online learning. Ieee Trans. Med. Imaging 2012, 31, 1698–1712. [Google Scholar] [CrossRef]
- Simkin, M.G.; Kuechler, W.L. Multiple-choice tests and student understanding: What is the connection? Decis. Sci. J. Innov. Educ. 2005, 3, 73–98. [Google Scholar] [CrossRef]
- Chiariotti, F.; D’Aronco, S.; Toni, L.; Frossard, P. Online learning adaptation strategy for DASH clients. In Proceedings of the 7th International Conference on Multimedia Systems, Klagenfurt, Austria, 10–13 May 2016; pp. 1–12. [Google Scholar]
- Sutton, R.S.; Whitehead, S.D. Online learning with random representations. In Proceedings of the Tenth International Conference on Machine Learning, Amherst, MA, USA, 24–29 June 1993; pp. 314–321. [Google Scholar]
- Tentama, F. Motivation to Learn and Social Support Determine Employability among Vocational High School Students. International. J. Eval. Res. Educ. 2019, 8, 237–242. [Google Scholar]
- Marbach-Ad, G.; Sokolove, P.G. Can undergraduate biology students learn to ask higher level questions? J. Res. Sci. Teach. Off. J. Natl. Assoc. Res. Sci. Teach. 2000, 37, 854–870. [Google Scholar] [CrossRef]
- Callahan, R.M. Tracking and high school English learners: Limiting opportunity to learn. Am. Educ. Res. J. 2005, 42, 305–328. [Google Scholar] [CrossRef] [Green Version]
- Yang, Q.; Chen, W.N.; Da Deng, J.; Li, Y.; Gu, T.; Zhang, J. A level-based learning swarm optimizer for large-scale optimization. IEEE Trans. Evol. Comput. 2017, 22, 578–594. [Google Scholar] [CrossRef]
- Lehre, P.K.; Nguyen, P.T.H. Level-based analysis of the population-based incremental learning algorithm. In Lecture Notes in Computer Science, Proceedings of the International Conference on Parallel Problem Solving from Nature, Coimbra, Portugal, 8–12 September 2018; Springer: Cham, Switzerland, 2018; pp. 105–116. [Google Scholar]
- Nasibov, E.N.; Baskan, O.; Mert, A. A learning algorithm for level sets weights in weighted level-based averaging method. Fuzzy Optim. Decis. Mak. 2005, 4, 279–291. [Google Scholar] [CrossRef]
- Park, S.H.; Kim, J.W.; Kim, D.H.; Cho, H.J. Music Therapy Counseling Recommendation Model Based on Collaborative Filtering. J. Korea Converg. Soc. 2019, 10, 31–36. [Google Scholar]
- Goldbeck, L.; Ellerkamp, T. A randomized controlled trial of multimodal music therapy for children with anxiety disorders. J. Music Ther. 2012, 49, 395–413. [Google Scholar] [CrossRef] [PubMed]
- Mott, M.S.; Robinson, D.H.; Walden, A.; Burnette, J.; Rutherford, A.S. Illuminating the effects of dynamic lighting on student learning. Sage Open 2012, 2, 2158244012445585. [Google Scholar] [CrossRef]
- Samani, S.A.; Samani, S.A. The impact of indoor lighting on students’ learning performance in learning environments: A knowledge internalization perspective. Int. J. Bus. Soc. Sci. 2012, 3, 127–136. [Google Scholar]
- Sleegers, P.J.; Moolenaar, N.M.; Galetzka, M.; Pruyn, A.; Sarroukh, B.E.; Van der Zande, B. Lighting affects students’ concentration positively: Findings from three Dutch studies. Light. Res. Technol. 2013, 45, 159–175. [Google Scholar] [CrossRef] [Green Version]
- Lee, J.; Yim, M.H.; Kim, J.Y. Test-retest reliability of the questionnaire in the Sasang constitutional analysis tool (SCAT). Integr. Med. Res. 2018, 7, 136–140. [Google Scholar] [CrossRef] [PubMed]
- Jin, H.J.; Baek, Y.; Kim, H.S.; Ryu, J.; Lee, S. Constitutional multicenter bank linked to Sasang constitutional phenotypic data. Bmc Complement. Altern. Med. 2015, 15, 1–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kang, M.; Yu, G.; Kim, L. A study on personality traits and cognitive characteristics of the Sasang Constitution using neuropsychological and personality tests. J. Orient. Neuropsychiatry 2015, 26, 131–142. [Google Scholar] [CrossRef]
- Choi, K.J.; Choi, Y.S.; Cha, J.H.; Hwang, M.W.; Lee, S.K.; Go, B.H.; Song, I.B. A Study on the Reliability and Validity test of the QSCC II + (Revised Questionnaire for the Sasang Constitution Classification). J. Sasang Const. Med. 2006, 18, 62–74. [Google Scholar]
- Swan, K.; Shen, J.; Hiltz, S.R. Assessment and collaboration in online learning. J. Asynchronous Learn. Netw. 2006, 10, 45–62. [Google Scholar] [CrossRef] [Green Version]
- Vonderwell, S.; Liang, X.; Alderman, K. Asynchronous discussions and assessment in online learning. J. Res. Technol. Educ. 2007, 39, 309–328. [Google Scholar] [CrossRef] [Green Version]
- Kearns, L.R. Student assessment in online learning: Challenges and effective practices. J. Online Learn. Teach. 2012, 8, 198. [Google Scholar]
- Goodfellow, R.; Lea, M.R. Supporting writing for assessment in online learning. Assess. Eval. High. Educ. 2005, 30, 261–271. [Google Scholar] [CrossRef]
- Graff, M. Cognitive style and attitudes towards using online learning and assessment methods. Electron. J. e-Learn. 2003, 1, 21–28. [Google Scholar]
Input Data | Level Test | |||
---|---|---|---|---|
Test Score | Item Difficulty | Wrong Answer Rate | Conventional Method | Proposed Method |
90 | Small | Big | Pass | Pass |
85 | Med | Big | Pass | Pass |
60 | Small | Big | Pass | Fail |
60 | Big | Big | Pass | Fail |
57 | Med | Small | Fail | Pass |
75 | Big | Big | Pass | Pass |
62 | Big | Big | Pass | Fail |
90 | Small | Big | Pass | Pass |
58 | Big | Big | Fail | Pass |
NAME | SCORE | DIFFICULTY | WRONG ANSWER | CORRECT ANSWER | SCORE INCREASING RATR | NON FUZZY | FUZZY |
---|---|---|---|---|---|---|---|
ALICE | 90 | SMALL | BIG | MEDIUM | SMALL | PASS | PASS |
TOMAS | 60 | BIG | SMALL | BIG | SMALL | PASS | FAIL |
CINDY | 50 | SMALL | BIG | SMALL | SMALL | FAIL | PASS |
KENT | 80 | MEDIUM | MEDIUM | SMALL | BIG | PASS | PASS |
JOHN | 55 | MEDIUM | SMALL | BIG | SMALL | PASS | FAIL |
LEE | 55 | SMALL | BIG | SMALL | SMALL | FAIL | PASS |
KIM | 80 | SMALL | BIG | MEDIUM | SMALL | PASS | PASS |
HONG | 70 | VERY BIG | BIG | MEDIUM | BIG | FAIL | PASS |
SMITH | 70 | MEDIUM | MEDIUM | SMALL | MEDIUM | PASS | PASS |
MARY | 65 | SMALL | BIG | SMALL | SMALL | FAIL | PASS |
ALICE | 60 | MEDIUM | BIG | MEDIUM | BIG | PASS | FAIL |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hong, Y.-S.; Han, C.-P.; Cho, S.-S. Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem. Appl. Sci. 2021, 11, 4380. https://doi.org/10.3390/app11104380
Hong Y-S, Han C-P, Cho S-S. Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem. Applied Sciences. 2021; 11(10):4380. https://doi.org/10.3390/app11104380
Chicago/Turabian StyleHong, You-Sik, Chang-Pyoung Han, and Seong-Soo Cho. 2021. "Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem" Applied Sciences 11, no. 10: 4380. https://doi.org/10.3390/app11104380
APA StyleHong, Y.-S., Han, C.-P., & Cho, S.-S. (2021). Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem. Applied Sciences, 11(10), 4380. https://doi.org/10.3390/app11104380