A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams
Abstract
:1. Introduction
2. Background
2.1. The Evaluation of Exams
2.1.1. Written Exams
2.2. Data Mining
2.3. Text Mining
- Clustering: It is the method to organize similar contents from unstructured data sources such as documents, news, images, paragraphs, sentences, comments, or terms in order to enhance retrieval and support browsing. It is the process to group the contents based on fuzzy information, such as words or word phrases in a set of documents. The similarity is computed using a similarity function. Measurement methods such as Cosine distance, Manhattan distance, and Euclidean distance are used for the clustering process. Besides, there is a wide variety of different clustering algorithms, such as hierarchical algorithms, partitioning algorithms, and standard parametric modeling-based methods. These algorithms are grouped along different dimensions based either on the underlying methodology of the algorithm, leading to agglomerative or partitional approaches, or on the structure of the final solution, leading to hierarchical or non-hierarchical solutions [35,38,43].
- Classification: It is a data mining approach to the grouping of the data according to the specified characteristics. It is carried out in two stages as learning and classification. In the first stage, a part of the data set is used for training purposes to determine how data characteristics will be classified. In the second stage, all datasets are classified by these rules. Supervised machine learning algorithms are used for classification methods. These algorithms include decision trees, I Bayes, nearest neighbor, classification and regression trees, support vector machines, and genetic algorithms [31,35,43].
- Summarization: It is used for documents and aims to determine the meanings, words, and phrases that can represent a document. It is carried out based on the language-specific rules that textual data belongs to. NLP approaches, which are one of the complex processes in terms of computer systems, are used in applications such as speech recognition, language translation, automated response systems, text summarization, and sentiment analysis [31,35,43].
2.4. Educational Data Mining (EDM)
3. Grey Relational Analysis (GRA)
4. Proposed Method
4.1. Data Collection
4.2. Data Preprocessing
4.3. Feature Extraction
4.3.1. Bag of Words (BoW)
Algorithm 1. BoW Generation |
|
4.3.2. Vector Space Model (VSM)
4.4. Analysis
5. Experimental Results
5.1. BoW Findings
5.2. VSM Findings
5.3. Grey Relational Degree Findings
6. Discussion and Suggestions
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
S | Q | Words (in both Turkish and English) |
1 | 1 | zihin_1& (mental_1&) |
3 | 1 | sosyal_1& (social_1&) |
6 | 1 | organizma_1&döl_1&başla_1& (organism_1&seed_1&start_1&) |
7 | 1 | başla_1&duygu_1& (start_1&emotion_1&) |
10 | 1 | döl_1&başla_1&sosyal_1&yön_1& (seed_1&start_1&social_1&direction_1&) |
13 | 1 | organizma_1&döl_1&başla_1&beden_1&zihin_1&sosyal_1&yön_1&geçir_1&sürekli_1&düz_1&değ_1&(organism_1&seed_1&start_1&body_1&mental_1&social_1&direction_1&pass_1&continuous_1&straight_1&touch_1&) |
14 | 1 | organizma_1&döl_1&başla_1&beden_1&zihin_1&duygu_1&sosyal_1&yön_1&geçir_1&sürekli_1&düz_2&ilerle_1&(organism_1&seed_1&start_1&body_1&mental_1&emotion_1&social_1&direction_1&pass_1&continuous_1&straight_2&move_1&) |
15 | 1 | döl_1&başla_1&sürekli_1& (seed_1&start_1&continuous_1&) |
18 | 1 | zihin_1& (mental_1&) |
19 | 1 | sosyal_1&yön_2& (social_1&direction_2&) |
20 | 1 | beden_1& (body_1&) |
21 | 1 | sosyal_1& (social_1&) |
24 | 1 | başla_1&zihin_1&sosyal_1& (start_1&mental_1&social_1&) |
25 | 1 | döl_1&başla_1& (seed_1&start_1&) |
26 | 1 | sürekli_1&düz_1&ilerle_1& (continuous_1&straight_1&move_1&) |
27 | 1 | organizma_1&döl_1&başla_1&sosyal_1&yön_1&geçir_1&sürekli_1&düz_1&değ_1&(organism_1&seed_1&start_1&social_1&direction_1&pass_1&continuous_1&straight_1&touch_1& |
29 | 1 | geçir_1& (pass_1&) |
31 | 1 | organizma_1&döl_1&başla_1&zihin_1&sosyal_1&yön_1&(organism_1&seed_1&start_1&mental_1&sosyal_1&yön_1&) |
32 | 1 | beden_1&zihin_1&sosyal_1&yön_1&geçir_1&sürekli_1&düz_1&değ_1&(body_1&mental_1&social_1&direction_1&pass_1&continuous_1&straight_1&touch_1&) |
33 | 1 | döl_1&başla_1&düz_1&ilerle_1& (seed_1&start_1& straight_1&move_1&) |
34 | 1 | başla_1&duygu_1&sosyal_1& (start_1&emotion_1&social_1&) |
37 | 1 | başla_1& (start_1&) |
39 | 1 | başla_1& (start_1&) |
40 | 1 | sosyal_1&yön_1& (social_1&direction_1&) |
43 | 1 | sürekli_1& (continuous_1&) |
44 | 1 | başla_1& (start_1&) |
45 | 1 | sürekli_1& (continuous_1&) |
46 | 1 | başla_2&duygu_1&sosyal_1& (start_2&emotion_1&social_1&) |
47 | 1 | duygu_1&sosyal_1&yön_1& (emotion_1&social_1&direction_1&) |
48 | 1 | döl_1&başla_1&sürekli_1&değ_1& (seed_1&start_1&continuous_1&touch_1&) |
49 | 1 | sosyal_1&yön_2& (social_1& direction_2&) |
50 | 1 | başla_1&duygu_1&sosyal_1&yön_1& (start_1&emotion_1&social_1&direction_1&) |
Words (in both Turkish and English) | ||||||||||||||
Q | S | Organizma (organism) | Döl (seed) | Başla (start) | Beden (body) | Zihin (mental) | Duygu (emotion) | Sosyal (social) | Yön (direction) | Geçir (pass) | Sürekli (continuous) | Düz (straight) | Değ (touch) | Ilerle (move) |
1 | 0 * | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 6 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 7 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 10 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
1 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 13 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
1 | 14 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 0 | 1 |
1 | 15 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
1 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 18 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 |
1 | 20 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 24 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 25 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 |
1 | 27 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
1 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
1 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 31 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
1 | 32 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
1 | 33 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
1 | 34 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 37 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 38 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 39 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
1 | 41 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 42 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
1 | 44 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 45 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
1 | 46 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 47 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
1 | 48 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
1 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 |
1 | 50 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
Appendix C
Question | Student | Words | ||||||||||||
1 | Answer Key | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
1 | Reference | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 |
1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Question | Student | Words | ||||||||||||
1 | Reference | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
Question | Student | Words | ||||||||||||
1 | Reference | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
1 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
Question | Student | Words | ||||||||||||
1 | Reference | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 1 | 1 | 1 | 0.333 | 1 | 0.333 | 1 | 1 | 0.333 | 1 | 0.333 | 0.333 | 1 | 1 |
1 | 2 | 1 | 1 | 0.333 | 1 | 1 | 1 | 1 | 0.333 | 1 | 0.333 | 0.333 | 1 | 1 |
1 | 3 | 1 | 1 | 0.333 | 1 | 1 | 1 | 0.333 | 0.333 | 1 | 0.333 | 0.333 | 1 | 1 |
1 | 4 | 1 | 1 | 0.333 | 1 | 1 | 1 | 1 | 0.333 | 1 | 0.333 | 0.333 | 1 | 1 |
1 | 5 | 1 | 1 | 0.333 | 1 | 1 | 1 | 1 | 0.333 | 1 | 0.333 | 0.333 | 1 | 1 |
Appendix D
S | Q-1 | Q-2 | Q-3 | Q-4 | Q-5 | Degrees |
1 | 0.4117 | 0.4189 | 0.5241 | 0.3874 | 0.3422 | 0.4169 |
2 | 0.3684 | 0.6305 | 0.4517 | 0.367 | 0.3859 | 0.4407 |
3 | 0.4117 | 0.4406 | 0.5863 | 0.4118 | 0.3692 | 0.4439 |
4 | 0.3684 | 0.6072 | 0.3692 | 0.4367 | 0.3913 | 0.4346 |
5 | 0.3684 | 1 | 0.4236 | 0.4011 | 0.3523 | 0.5091 |
6 | 0.4117 | 0.4006 | 0.4745 | 0.3697 | 0.3778 | 0.4069 |
7 | 0.3684 | 0.4521 | 0.5029 | 0.3741 | 0.4045 | 0.4204 |
8 | 0.3684 | 0.709 | 0.4264 | 0.4037 | 0.3822 | 0.4579 |
9 | 0.3684 | 0.4106 | 0.6004 | 0.3964 | 0.377 | 0.4306 |
10 | 0.3684 | 0.7275 | 1 | 0.3923 | 0.4133 | 0.5803 |
11 | 0.3684 | 0.4362 | 0.4236 | 0.3695 | 0.4592 | 0.4114 |
12 | 0.3684 | 0.4545 | 0.6159 | 0.3846 | 0.3488 | 0.4344 |
13 | 0.5385 | 0.6535 | 0.3446 | 0.4022 | 0.48 | 0.4838 |
14 | 1 | 0.621 | 0.5699 | 1 | 1 | 0.8382 |
15 | 0.3333 | 0.7404 | 0.3486 | 0.3864 | 0.3913 | 0.44 |
16 | 0.3684 | 0.4757 | 0.6159 | 0.3641 | 0.4059 | 0.446 |
17 | 0.3684 | 0.4812 | 0.5029 | 0.3991 | 0.3536 | 0.421 |
18 | 0.4117 | 0.5224 | 0.5132 | 0.3854 | 0.3488 | 0.4363 |
19 | 0.4117 | 0.5224 | 0.5221 | 0.42 | 0.3505 | 0.4453 |
20 | 0.4117 | 0.3812 | 0.4402 | 0.3966 | 0.3818 | 0.4023 |
21 | 0.4117 | 0.4524 | 0.4008 | 0.4123 | 0.3333 | 0.4021 |
22 | 0.3684 | 0.4295 | 0.4668 | 0.3623 | 0.3711 | 0.3996 |
23 | 0.3684 | 0.4247 | 0.4891 | 0.3978 | 0.381 | 0.4122 |
24 | 0.4117 | 0.4298 | 0.4531 | 0.4123 | 0.379 | 0.4172 |
25 | 0.3684 | 0.6078 | 0.4813 | 0.443 | 0.4433 | 0.4688 |
26 | 0.3333 | 0.5224 | 0.3535 | 0.4123 | 0.3987 | 0.404 |
27 | 0.4117 | 0.7312 | 0.3333 | 0.4393 | 0.3523 | 0.4536 |
28 | 0.3684 | 0.3812 | 0.3566 | 0.3741 | 0.3863 | 0.3733 |
29 | 0.4117 | 0.5949 | 0.4252 | 0.4084 | 0.4045 | 0.4489 |
30 | 0.3684 | 0.4423 | 0.5627 | 0.4123 | 0.379 | 0.4329 |
31 | 0.4666 | 0.3954 | 0.3867 | 0.3333 | 0.4374 | 0.4039 |
32 | 0.4666 | 0.3826 | 0.3513 | 0.4051 | 0.3673 | 0.3946 |
33 | 0.3684 | 0.616 | 0.6004 | 0.3944 | 0.3913 | 0.4741 |
34 | 0.4117 | 0.6444 | 0.4531 | 0.3856 | 0.4348 | 0.4659 |
35 | 0.3684 | 0.4278 | 0.5728 | 0.3792 | 0.5389 | 0.4574 |
36 | 0.3684 | 0.4329 | 0.3805 | 0.3968 | 0.3913 | 0.394 |
37 | 0.3333 | 0.4683 | 0.5404 | 0.3964 | 0.3913 | 0.4259 |
38 | 0.3684 | 0.616 | 0.3627 | 0.3921 | 0.3913 | 0.4261 |
39 | 0.3333 | 0.5224 | 0.5358 | 0.4492 | 0.4068 | 0.4495 |
40 | 0.3684 | 0.4112 | 0.6159 | 0.4123 | 0.3913 | 0.4398 |
41 | 0.3684 | 0.6305 | 0.5378 | 0.4196 | 0.3818 | 0.4676 |
42 | 0.3684 | 0.4647 | 0.4319 | 0.4239 | 0.3913 | 0.416 |
43 | 0.3333 | 0.4647 | 0.4382 | 0.4227 | 0.3913 | 0.41 |
44 | 0.3333 | 0.4647 | 0.4833 | 0.3731 | 0.3913 | 0.4091 |
45 | 0.3333 | 0.4861 | 0.3928 | 0.4123 | 0.422 | 0.4093 |
46 | 0.4666 | 0.3974 | 0.4929 | 0.4013 | 0.3913 | 0.4299 |
47 | 0.4117 | 0.5291 | 0.5241 | 0.4123 | 0.355 | 0.4464 |
48 | 0.3684 | 0.549 | 0.4467 | 0.4123 | 0.4245 | 0.4402 |
49 | 0.4117 | 0.3812 | 0.3952 | 0.4256 | 0.3987 | 0.4025 |
50 | 0.3684 | 0.3333 | 0.4097 | 0.4013 | 0.3913 | 0.3808 |
Evaluator Scores | GRA Results | |||||||||
S | Q-1 | Q-2 | Q-3 | Q-4 | Q-5 | Total | Rank | Degrees | Rank | Suggestion |
1 | 5 | 8 | 5 | 0 | 2 | 20 | 33 | 0.4169 | 33 | do not change |
2 | 0 | 10 | 10 | 15 | 0 | 35 | 25 | 0.4407 | 18 | increase the score |
3 | 15 | 12 | 10 | 0 | 0 | 37 | 24 | 0.4439 | 17 | increase the score |
4 | 5 | 0 | 15 | 15 | 0 | 35 | 26 | 0.4346 | 23 | increase the score |
5 | 0 | 20 | 12 | 13 | 20 | 65 | 8 | 0.5091 | 3 | increase the score |
6 | 10 | 8 | 10 | 15 | 10 | 53 | 13 | 0.4069 | 40 | decrease the score |
7 | 15 | 15 | 20 | 15 | 2 | 67 | 7 | 0.4204 | 31 | decrease the score |
8 | 0 | 0 | 10 | 15 | 10 | 35 | 27 | 0.4579 | 9 | increase the score |
9 | 10 | 20 | 0 | 0 | 2 | 32 | 29 | 0.4306 | 26 | increase the score |
10 | 20 | 20 | 20 | 20 | 20 | 100 | 1 | 0.5803 | 2 | decrease the score |
11 | 0 | 15 | 20 | 15 | 20 | 70 | 6 | 0.4114 | 36 | decrease the score |
12 | 0 | 15 | 0 | 10 | 15 | 50 | 14 | 0.4344 | 24 | decrease the score |
13 | 15 | 20 | 20 | 15 | 15 | 85 | 3 | 0.4838 | 4 | decrease the score |
14 | 20 | 20 | 12 | 15 | 20 | 87 | 2 | 0.8382 | 1 | increase the score |
15 | 10 | 10 | 5 | 5 | 10 | 40 | 16 | 0.44 | 20 | decrease the score |
16 | 0 | 15 | 20 | 0 | 5 | 40 | 17 | 0.446 | 15 | increase the score |
17 | 0 | 5 | 0 | 0 | 0 | 5 | 48 | 0.421 | 30 | increase the score |
18 | 0 | 0 | 5 | 15 | 5 | 25 | 32 | 0.4363 | 22 | increase the score |
19 | 10 | 0 | 0 | 0 | 0 | 10 | 45 | 0.4453 | 16 | increase the score |
20 | 5 | 8 | 0 | 5 | 0 | 18 | 37 | 0.4023 | 44 | decrease the score |
21 | 15 | 2 | 20 | 0 | 2 | 39 | 21 | 0.4021 | 45 | decrease the score |
22 | 0 | 15 | 5 | 10 | 10 | 40 | 18 | 0.3996 | 46 | decrease the score |
23 | 0 | 8 | 0 | 0 | 0 | 8 | 46 | 0.4122 | 35 | increase the score |
24 | 0 | 8 | 5 | 0 | 0 | 13 | 42 | 0.4172 | 32 | increase the score |
25 | 10 | 10 | 15 | 15 | 15 | 65 | 9 | 0.4688 | 6 | increase the score |
26 | 10 | 0 | 10 | 0 | 10 | 30 | 31 | 0.404 | 41 | decrease the score |
27 | 20 | 20 | 20 | 15 | 10 | 85 | 4 | 0.4536 | 11 | decrease the score |
28 | 10 | 8 | 5 | 10 | 0 | 33 | 28 | 0.3733 | 50 | decrease the score |
29 | 0 | 18 | 0 | 8 | 5 | 31 | 30 | 0.4489 | 13 | increase the score |
30 | 0 | 10 | 10 | 0 | 0 | 20 | 34 | 0.4329 | 25 | increase the score |
31 | 18 | 10 | 20 | 20 | 15 | 83 | 5 | 0.4039 | 42 | decrease the score |
32 | 18 | 15 | 10 | 5 | 2 | 50 | 15 | 0.3946 | 47 | decrease the score |
33 | 10 | 15 | 10 | 20 | 0 | 55 | 11 | 0.4741 | 5 | increase the score |
34 | 10 | 4 | 10 | 20 | 10 | 54 | 12 | 0.4659 | 8 | increase the score |
35 | 10 | 0 | 0 | 10 | 10 | 40 | 19 | 0.4574 | 10 | increase the score |
36 | 0 | 5 | 10 | 0 | 0 | 15 | 40 | 0.394 | 48 | decrease the score |
37 | 10 | 2 | 0 | 0 | 0 | 12 | 43 | 0.4259 | 29 | increase the score |
38 | 0 | 18 | 0 | 0 | 0 | 18 | 38 | 0.4261 | 28 | increase the score |
39 | 0 | 0 | 10 | 5 | 5 | 20 | 35 | 0.4495 | 12 | increase the score |
40 | 0 | 5 | 0 | 0 | 0 | 5 | 49 | 0.4398 | 21 | increase the score |
41 | 0 | 12 | 0 | 0 | 0 | 12 | 44 | 0.4676 | 7 | increase the score |
42 | 0 | 6 | 0 | 0 | 0 | 6 | 47 | 0.416 | 34 | increase the score |
43 | 0 | 6 | 14 | 0 | 0 | 20 | 36 | 0.41 | 37 | decrease the score |
44 | 0 | 5 | 0 | 0 | 0 | 5 | 50 | 0.4091 | 39 | increase the score |
45 | 0 | 10 | 20 | 0 | 10 | 40 | 20 | 0.4093 | 38 | decrease the score |
46 | 10 | 18 | 5 | 5 | 0 | 38 | 22 | 0.4299 | 27 | decrease the score |
47 | 0 | 8 | 5 | 0 | 2 | 15 | 41 | 0.4464 | 14 | increase the score |
48 | 20 | 15 | 15 | 0 | 10 | 60 | 10 | 0.4402 | 19 | decrease the score |
49 | 0 | 8 | 10 | 0 | 0 | 18 | 39 | 0.4025 | 43 | decrease the score |
50 | 10 | 8 | 15 | 5 | 0 | 38 | 23 | 0.3808 | 49 | decrease the score |
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Advantages | Disadvantages |
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Advantages | Disadvantages |
---|---|
|
|
Advantages | Disadvantages |
---|---|
|
|
Question | Student | Words (in Both Turkish and English) | |||
---|---|---|---|---|---|
Zihin (Mental) | Sosyal (Social) | Duygu (Emotion) | |||
Answer key | 1 | 0 | 1 | 1 | 1 |
1 | 1 | 1 | 0 | 0 | |
1 | 2 | 0 | 0 | 0 | |
1 | 3 | 0 | 1 | 0 | |
1 | 4 | 0 | 0 | 0 | |
1 | 5 | 0 | 0 | 0 |
Student | Question-1 | Question-2 | Question-3 | Question-4 | Question-5 |
---|---|---|---|---|---|
1 | 0.7436 | 0.8921 | 0.7357 | 0.8317 | 0.9022 |
2 | 0.7949 | 0.789 | 0.7532 | 0.8423 | 0.8903 |
3 | 0.7436 | 0.8769 | 0.7241 | 0.8204 | 0.8945 |
4 | 0.7949 | 0.7968 | 0.7815 | 0.8102 | 0.889 |
5 | 0.7949 | 0.7136 | 0.7616 | 0.8252 | 0.8992 |
Student | Question-1 | Question-2 | Question-3 | Question-4 | Question-5 | Degrees |
---|---|---|---|---|---|---|
1 | 0.4117 | 0.4189 | 0.5241 | 0.3874 | 0.3422 | 0.4169 |
2 | 0.3684 | 0.6305 | 0.4517 | 0.367 | 0.3859 | 0.4407 |
3 | 0.4117 | 0.4406 | 0.5863 | 0.4118 | 0.3692 | 0.4439 |
4 | 0.3684 | 0.6072 | 0.3692 | 0.4367 | 0.3913 | 0.4346 |
5 | 0.3684 | 1 | 0.4236 | 0.4011 | 0.3523 | 0.5091 |
Evaluator Scores | Analysis Results | ||||||
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Student | Question-1 | Question-2 | Question-3 | Question-4 | Question-5 | Total | GRA Degrees |
1 | 5 | 8 | 5 | 0 | 2 | 20 | 0.4169 |
2 | 0 | 10 | 10 | 15 | 0 | 35 | 0.4407 |
3 | 15 | 12 | 10 | 0 | 0 | 37 | 0.4439 |
4 | 5 | 0 | 15 | 15 | 0 | 35 | 0.4346 |
5 | 0 | 20 | 12 | 13 | 20 | 65 | 0.5091 |
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Yuksel, M.E.; Fidan, H. A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams. Symmetry 2019, 11, 1426. https://doi.org/10.3390/sym11111426
Yuksel ME, Fidan H. A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams. Symmetry. 2019; 11(11):1426. https://doi.org/10.3390/sym11111426
Chicago/Turabian StyleYuksel, Mehmet Erkan, and Huseyin Fidan. 2019. "A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams" Symmetry 11, no. 11: 1426. https://doi.org/10.3390/sym11111426