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Journal articles on the topic 'Evaluation model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 September 2024

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1

NICA, Panaite, and Silviu Mihail TITA. "Efficiency – Model for Scientific Research Evaluation." Revista Romaneasca pentru Educatie Multidimensionala 5, no. 1 (June 30, 2013): 141–59. http://dx.doi.org/10.18662/rrem/2013.0501.10.

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2

Lahajnar, Sebastian, and Alenka Rožanec. "Initial Coin Offering (ICO) evaluation model." Investment Management and Financial Innovations 15, no. 4 (November 19, 2018): 169–82. http://dx.doi.org/10.21511/imfi.15(4).2018.14.

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High-tech companies operating in the field of blockchain technology use the Initial Coin Offering (ICO) to raise start-up capital. It is a fairly new, non-standardized and poorly regulated way of collecting start-up funding that can bring high yields to investors in the short term, but investors also have to be ready to take on high risks. The purpose of this article is to define a decision model for the evaluation of ICO projects, which provides a systematic, transparent, methodological approach to making decisions on investing in them. For that purpose, the authors analyzed a number of factors, which directly or indirectly influence the successful implementation of ICO projects, and the researchers extracted the most important among them (model parameters). In order to build the decision model, used a qualitative method for the hierarchical multi-parameter evaluation of DEX, which using symbolic parameters and combining functions in the form of if-then rules ensures the most freely and flexible combining assessment parameters into a uniform model. In the article, the use of proposed decision model was tested in practice on multiple ICO processes. The article details the decision-making process in the case of CargoX, and also summarizes the results of the evaluation of ICOs Tokens.net, BitClave, Neuromation and WePower.

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3

Masood Butt, Saad, Shahid Masood Butt, Azura Onn, Nadra Tabassam, and Mazlina Abdul Majid. "Usability Evaluation Techniques for Agile Software Model." Journal of Software 10, no. 1 (January 2015): 32–41. http://dx.doi.org/10.17706/jsw.10.1.32-41.

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4

Munro, Barbara Hazard. "Evaluation Model." Clinical Nurse Specialist 9, no. 3 (May 1995): 144. http://dx.doi.org/10.1097/00002800-199505000-00007.

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5

Sparks, Elizabeth, Michelle Molina, Natalie Shepp, and Fiona Davey. "The Evaluation Skill-a-Thon: Evaluation Model for Meaningful Youth Engagement." Journal of Youth Development 16, no. 1 (March 30, 2021): 100–125. http://dx.doi.org/10.5195/jyd.2021.968.

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Active engagement of youth participants in the evaluation process is an increasingly sought out method, but the field can still benefit from new methods that ease youth participatory evaluation implementation. Meaningful youth engagement in the evaluation process is particularly advantageous under the 4-H thriving model because of its potential to contribute to positive youth development, foster relationship building, enhance evaluation capacity, and improve program quality through improved evaluations. This program sought to facilitate actively engaging youth in the evaluation process by breaking it up into clear and manageable steps including evaluation design, data collection, data interpretation and analysis, reporting results, and outlining programmatic change. To achieve this aim, program staff designed the Evaluation Skill-a-Thon, a set of self-paced, experiential evaluation activities at various stations through which youth participants rotate. Actively involving youth participants in the evaluation process using the Skill-a-Thon model resulted in youth being able to identify and design programmatic changes, increased participation and response rates in other evaluations, and several youth participants’ gaining an interest in evaluation and working to design evaluations in later years. The Evaluation Skill-a-Thon holds promise for actively engaging youth participants in the entire evaluation process, easy implementation, and increasing evaluation capacity.

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6

Lim, Hee-Jeong, Young-Hee Lee, and Hyo-Jung Kwon. "Evaluation of Community Land Model version 3.5-Dynamic Global Vegetation Model over Deciduous Forest in Gwangneung, Korea." Korean Journal of Agricultural and Forest Meteorology 12, no. 2 (June 30, 2010): 95–106. http://dx.doi.org/10.5532/kjafm.2010.12.2.095.

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7

Turgut, Mehmet, and Alp Ustundag. "A Hybrid Risk Evaluation Model for Automotive Production." International Journal of Machine Learning and Computing 4, no. 5 (2014): 458–62. http://dx.doi.org/10.7763/ijmlc.2014.v4.454.

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8

Tvrdoň, J. "Some model approaches of natural resources efficiency evaluation." Agricultural Economics (Zemědělská ekonomika) 51, No. 2 (February 20, 2012): 47–50. http://dx.doi.org/10.17221/5075-agricecon.

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The article deals with problems of sustainable development of agriculture from the viewpoint of efficient use of environmental resources. Environmental resources are factors of creation both usual economic goods and stabilization of environment. Analysis proved that classical indicators of economic performance are necessary to be modified by the ecological threshold of economic activities. However its level is not fixed and follows factors introduced in the paper which arose in frame of solution of the Institutional Research Intention MSM 411100013.

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9

Kozyk, V., O. Mrykhina, and T. Mirkunova. "Conceptual Model for Economic Evaluation of Innovative Technologies." Economics, Entrepreneurship, Management 4, no. 2 (2017): 45–58. http://dx.doi.org/10.23939/eem2017.02.045.

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10

Icke, J., K. J. van Heeringen, S. Groot, and M. Ouboter. "Model Evaluation Tool experiences for the Amstel River." River Systems 17, no. 1-2 (July 28, 2006): 97–113. http://dx.doi.org/10.1127/lr/17/2006/97.

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11

K. J., Sarvamangala, and G. Sudarsana Reddy. "Performance Evaluation of Select Banks using CAMELS Model." International Journal of Research Publication and Reviews 5, no. 5 (May 26, 2024): 11653–60. http://dx.doi.org/10.55248/gengpi.5.0524.1419.

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12

Stanojević, Petar, and Slobodan Ilić. "Evaluation model of maintenance system efficiency." Vojnotehnicki glasnik 45, no. 1 (1997): 23–36. http://dx.doi.org/10.5937/vojtehg9701023s.

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13

Ridge, Richard S., Gary A. Stern, and Ronald K. Watts. "Econometric Model Evaluation." Evaluation Review 14, no. 3 (June 1990): 308–14. http://dx.doi.org/10.1177/0193841x9001400306.

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14

徐, 宇明. "Wine Evaluation Model." Statistics and Application 06, no. 01 (2017): 104–10. http://dx.doi.org/10.12677/sa.2017.61012.

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15

Lodge, Milton, Kathleen M. McGraw, and Patrick Stroh. "An Impression-Driven Model of Candidate Evaluation." American Political Science Review 83, no. 2 (June 1989): 399–419. http://dx.doi.org/10.2307/1962397.

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We describe and test two process models of candidate evaluation. The memory-based model holds that evaluations are dependent on the mix of pro and con information retrieved from memory. The impression-driven model holds that evaluations are formed and updated “on-line” as information is encountered. The results provide evidence for the existence of stereotyping and projection biases that render the mix of evidence available in memory a nonveridical representation of the information to which subjects were exposed. People do not rely on the specific candidate information available in memory. Rather, consistent with the logic of the impression-driven processing model, an “on-line” judgment formed when the information was encountered best predicts candidate evaluation. The results raise both methodological and substantive challenges to how political scientists measure and model the candidate evaluation process.

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16

Finney, Tia L. "Confirmative Evaluation: New CIPP Evaluation Model." Journal of Modern Applied Statistical Methods 18, no. 2 (December 7, 2020): 2–24. http://dx.doi.org/10.22237/jmasm/1598889893.

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Struggling trainees often require a substantial investment of time, effort, and resources from medical educators. An emergent challenge involves developing effective ways to accurately identify struggling students and better understand the primary causal factors underlying their poor performance. Identifying the potential reasons for poor performance in medical school is a key first step in developing suitable remediation plans. The SOM Modified Program is a remediation program that aims to ensure academic success for medical students. The purpose of this study is to determine the impact of modifying the CIPP evaluation model by adding a confirmative evaluation step to the model. This will be carried out by conducting a program evaluation of Wayne State University’s School of Medicine Modified Program to determine its effectiveness for student success. The key research questions for this study are 1) How effective is the Modified Program for student’s success in the SOM? 2) Do students benefit from a modified program in medical school? 3) Will the CIPP program evaluation model become more effective by adding confirmative evaluation component?

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17

Wolf, A., K. Fackler, M. Reulbach, S. Wartzack, and J. Miehling. "Computer Aided Ergonomics: Evaluation Study of a Interaction Model for Digital Human Models." Proceedings of the Design Society 2 (May 2022): 663–72. http://dx.doi.org/10.1017/pds.2022.68.

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AbstractIn user-centred design, digital human models hold the potential for proactive evaluations of ergonomics or discomfort in terms of a computer aided ergonomics tool. Therefore, models predicting human interaction behaviour, are necessary. In this contribution we present such a model as well as its initial evaluation. The evaluation is performed by applying the interaction model to a specific use case, conducting a comparison with an empirical subject study. The evaluation shows that similar and realistic human behaviour was predicted, which was consistent in terms of whole-body strain.

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18

Deng, Baichuan, Hongmei Lu, Chengquan Tan, Jinping Deng, and Yulong Yin. "Model population analysis in model evaluation." Chemometrics and Intelligent Laboratory Systems 172 (January 2018): 223–28. http://dx.doi.org/10.1016/j.chemolab.2017.11.016.

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19

Feng, J., X. B. Zhan, Z. Y. Zheng, D. Wang, L. M. Zhang, and C. C. Lin. "New model for flavour quality evaluation of soy sauce." Czech Journal of Food Sciences 31, No. 3 (May 22, 2013): 292–305. http://dx.doi.org/10.17221/524/2011-cjfs.

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The soy sauce samples established a model for its flavour quality evaluation. Initially, 39 types of flavour compounds, organic acids and free amino acids in six different types of soy sauce were identified and determined by HS-SPME GC/MS and HPLC. The model was developed based on the principal component analysis method for assessing and ranking of flavour quality of soy sauce. Using the principal component analysis which simplifies complex information, our correlative evaluation model was established, tested by comparing the traditional sensory evaluation method, providing a new methodology for objective evaluation of the flavour quality of soy sauce.  

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20

Kim. "Formulation and Evaluation of Railway Optimal Alignment Design Model." Journal of the Korean Society of Civil Engineers 34, no. 6 (2014): 1851. http://dx.doi.org/10.12652/ksce.2014.34.6.1851.

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21

Mangmang, Geraldine B. "Internship Performance Evaluation: A Predictive Model for IT Students." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1123–26. http://dx.doi.org/10.5373/jardcs/v11sp10/20192914.

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22

Emmert-Streib, Frank, and Matthias Dehmer. "Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error." Machine Learning and Knowledge Extraction 1, no. 1 (March 22, 2019): 521–51. http://dx.doi.org/10.3390/make1010032.

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When performing a regression or classification analysis, one needs to specify a statistical model. This model should avoid the overfitting and underfitting of data, and achieve a low generalization error that characterizes its prediction performance. In order to identify such a model, one needs to decide which model to select from candidate model families based on performance evaluations. In this paper, we review the theoretical framework of model selection and model assessment, including error-complexity curves, the bias-variance tradeoff, and learning curves for evaluating statistical models. We discuss criterion-based, step-wise selection procedures and resampling methods for model selection, whereas cross-validation provides the most simple and generic means for computationally estimating all required entities. To make the theoretical concepts transparent, we present worked examples for linear regression models. However, our conceptual presentation is extensible to more general models, as well as classification problems.

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23

Mardiah, Mardiah, and Syarifudin Syarifudin. "MODEL-MODEL EVALUASI PENDIDIKAN." MITRA ASH-SHIBYAN: Jurnal Pendidikan dan Konseling 2, no. 1 (January 23, 2019): 38–50. http://dx.doi.org/10.46963/mash.v2i1.24.

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merupakan salah satu komponen dari sistem pendidikan Islam yang harus dilakukan secara sistematis dan terencana sebagai alat untuk mengukur keberhasilan atau target yang akan dicapai dalam proses pendidikan atau proses pembelajaran. Model-model evaluasi pendidikan adalah contoh atau pola acuan suatu proses penilaian dalam mengumpulkan dan menganalisis untuk menentukan taraf kemajuan suatu aktivitas di dalam pendidikan guna menetapkan pencapaian suatu tujuan baik untuk pendidik dan peserta didik. Adapun model-model evaluasi pendidikan adalah Goal Oriented Evaluation Model, Goal Free Evaluation Model, Formatif Sumatif Evaluation, dan Countenance Evaluation Model.

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24

KURUM, Gul, and Sakir CINKIR. "An Authentic Look at Evaluation in Education: A School Self- Evaluation Model Supporting School Development." Eurasian Journal of Educational Research 19, no. 83 (October 1, 2019): 1–34. http://dx.doi.org/10.14689/ejer.2019.83.12.

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25

Tochot, P., P. Junpeng, and P. Makmee. "Measurement Model of Evaluation Utilization: External Evaluation." Procedia - Social and Behavioral Sciences 69 (December 2012): 1751–56. http://dx.doi.org/10.1016/j.sbspro.2012.12.124.

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26

Tutulmaz, Onur. "Monetary Growth Models: An Evaluation on Tobin Model." JOURNAL OF SOCIAL SCIENCE RESEARCH 6, no. 2 (December 30, 2014): 1029–35. http://dx.doi.org/10.24297/jssr.v6i2.3485.

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Growth models have an important place in the economics. These models try to reflect the behavior of an economy so that we can understand and analyze the growth mechanism in the economy. Growth theory first focusing on the production function tried to explain the growth in the physical production. However, in the real world, if the saving part of that production is not channeled into physical capital, the scenario of the early growth models cannot reflect the real world. In this sense, Tobin model has made the first step including money option for the individual saving portfolio and turned the growth model into the monetary growth model. In this study, we revisited important discussions on Tobin model and by conducting a critical analysis on the critically important features we are hoping to attract attention to the uncompleted arguments. In this manner we evaluate that the stableness of the equilibrium can be translated differently so that point needs to be cleared in described classification. The critical first step of articulation of saving portfolio can put the model in a strategic reference point situation as we would like to underline.

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27

Hu, Jinxiang, M. David Miller, Anne Corinne Huggins-Manley, and Yi-Hsin Chen. "Evaluation of Model Fit in Cognitive Diagnosis Models." International Journal of Testing 16, no. 2 (February 17, 2016): 119–41. http://dx.doi.org/10.1080/15305058.2015.1133627.

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28

Wiggans, G. R., and P. M. VanRaden. "Animal Model Evaluation Within Herd Linked to National Evaluations." Journal of Dairy Science 73, no. 7 (July 1990): 1956–63. http://dx.doi.org/10.3168/jds.s0022-0302(90)78874-1.

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29

Samon, Jean Bosco, and Brice Landry Tekam Guessom. "Evaluation Model of Mechatronizability." Journal of Mechatronics and Robotics 6, no. 1 (January 1, 2022): 65–78. http://dx.doi.org/10.3844/jmrsp.2022.65.78.

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30

Kellogg, Nancy D., Mary Jane Chapa, Patricia Metcalf, Michelle Trotta, and Daisy Rodriguez. "Medical/Social Evaluation Model." Journal of Child Sexual Abuse 2, no. 4 (January 10, 1994): 1–17. http://dx.doi.org/10.1300/j070v02n04_01.

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31

Mulaik, Stanley A. "Parsimony and Model Evaluation." Journal of Experimental Education 66, no. 3 (January 1998): 266–73. http://dx.doi.org/10.1080/00220979809604411.

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32

Perry, Jan F., and Carol Endebrock Lee. "Model for Curriculum Evaluation." Journal of Physical Therapy Education 2, no. 2 (July 1988): 13–17. http://dx.doi.org/10.1097/00001416-198807000-00004.

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33

Moldovan, Liviu. "Training Outcome Evaluation Model." Procedia Technology 22 (2016): 1184–90. http://dx.doi.org/10.1016/j.protcy.2016.01.166.

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34

Nakamura, Alice, and James R. Walker. "Model Evaluation and Choice." Journal of Human Resources 29, no. 2 (1994): 223. http://dx.doi.org/10.2307/146097.

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35

Petersen, K. E. "European model evaluation activity." Journal of Loss Prevention in the Process Industries 7, no. 2 (January 1994): 130–32. http://dx.doi.org/10.1016/0950-4230(94)80029-4.

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36

Ovezgel’dyev, A. O., and K. É. Petrov. "Adaptive multifactor-evaluation model." Cybernetics and Systems Analysis 33, no. 3 (May 1997): 379–85. http://dx.doi.org/10.1007/bf02733071.

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37

Sieloff, Debra A. "The bridge evaluation model." Performance Improvement 38, no. 10 (November 1999): 14–17. http://dx.doi.org/10.1002/pfi.4140381004.

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38

KOIWA, Kozo, Hiroshi TANAKA, Yoshikazu NAKAI, Shinya ITO, and Takeshi TUKAHARA. "OS12F018 Fracture Mechanics Evaluation of Mode I and Mode II Fiber/Matrix Interfacial Crack by Using Real-Size Model Composite." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2011.10 (2011): _OS12F018——_OS12F018—. http://dx.doi.org/10.1299/jsmeatem.2011.10._os12f018-.

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39

Chitra, K., and J. Manoranjini. "Evaluation of Nursing Practice by Using Betty Neuman System Model." Asian Pacific Journal of Health Sciences 6, no. 1 (March 2019): 55–57. http://dx.doi.org/10.21276/apjhs.2019.6.1.8.

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40

Dahmoune, Badreddine, and Hamidi Mansour. "ALGERIAN NORTHWESTERN SEISMIC HAZARD EVALUATION BASED ON THE MARKOV MODEL." Rudarsko-geološko-naftni zbornik 34, no. 1 (2019): 113–25. http://dx.doi.org/10.17794/rgn.2019.1.10.

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41

Cupertino, César Medeiros, and Paulo Roberto Barbosa Lustosa. "The Ohlson Model of Evaluation of Companies: Tutorial for Use." Brazilian Business Review 1, no. 1 (January 30, 2004): 1–16. http://dx.doi.org/10.15728/bbr.2004.1.1.1.

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42

Kim, Jeong-Yeop, Sang-Hyun Kim, and Gil-Ja So. "Evaluation of Perceived Depth Model with Change of Hue Information." International Journal of Computer and Communication Engineering 3, no. 1 (2014): 75–80. http://dx.doi.org/10.7763/ijcce.2014.v3.295.

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43

Kumaraswamy, Sumathi, and Ibrahim Al Ezee. "Performance evaluation of Saudi equity mutual funds: Fama decomposition model." Investment Management and Financial Innovations 15, no. 4 (November 16, 2018): 158–68. http://dx.doi.org/10.21511/imfi.15(4).2018.13.

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This paper is in pursuit of analyzing and elongating prior research on the performance evaluation of mutual funds by a comparative analysis with three categories of 82 Saudi equity funds during 2011 to 2016 using Fama’s decomposition model. The paper also made an attempt to explore the relationship with the risk reward ratio to the relative performance measure in predicting the future performance of the Saudi equity fund returns. The empirical results show that Saudi local equity funds perform better followed by Arabian and international/global equity funds in terms of expected signs and diagnostic tests.

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44

Kang, Qixiu, Jing Tang, and Yuming Wang. "Product Promotion Prediction Model Based on Evaluation Information." Modern Electronic Technology 5, no. 1 (May 6, 2021): 10. http://dx.doi.org/10.26549/met.v5i1.6373.

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This paper mainly studies the impact of evaluation information on e-commerce platform on the future of products. Through natural language processing and rating, an evaluation model based on user rating and evaluation is defined to measure product quality. Among them, evaluations are differentiated: review sentiment coefficient (R) and review length (L).The evaluation model is:D=0.3*S+0.7*( 0.3*L+0.7*R). In order to predict the future reputation of products, based on the above evaluation model, time series is used to rank the products studied. Each customer purchases the product through Markov chain model, so as to predict the probability of future word-of-mouth spread of the product. Use TOPSIS method to select monthly sales, stars and comment sentiment coefficient as indicators. The comprehensive measurement method based on text and score is determined to predict whether the product is successfully promoted.

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45

Iqbal, Muhammad, Intan Savitri, Fathia Hanifah, Nur Alfiana Kholizah, Dinda Aulia Sari, and Tarisa Munawwarah. "Model Evaluasi Program Pendidikan Terhadap Peningkatan Kualitas Pembelajaran." Indo-MathEdu Intellectuals Journal 5, no. 3 (July 6, 2024): 3535–40. http://dx.doi.org/10.54373/imeij.v5i3.1472.

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The quality of education in an educational institution is currently one of the important elements in the sustainability of the educational institution itself, both in terms of the quality of infrastructure, the quality of teaching and education staff or the quality of learning, all of this is one unit that must be maintained and updated in order to make education quality. An educational evaluation model, where matters related to evaluation data are collected is the main step in conducting educational evaluations, especially the quality of learning. Through research using this qualitative method, the author intends to provide readers with views regarding educational evaluation models, especially evaluations that lead to improving the quality of learning with appropriate steps. Evaluation models may appear to vary from one another, but the aims and objectives are the same, namely carrying out data or information collection activities relating to the object being evaluated.

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46

Liu, Xin Xiong, Qing Yun Liu, Wei Wang, Cong Mei Huang, and Wen Si Li. "Research on the E-Commerce's User Experience Evaluation Model." Applied Mechanics and Materials 427-429 (September 2013): 2859–63. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2859.

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Firstly, the current situations of E-commerces user experience evaluation model were analyzed. And then, fuzzy comprehensive analyses were introduced. After that, an improved E-commerces user experience evaluation model based on fuzzy comprehensive analysis was established. In improved model, user experience hierarchy model was created based on psychological-stratification, experts fuzzy evaluations of evaluation factors were integrated into fuzzy matrix by using the theory of Triangular Fuzzy Numbers. Finally an example is given to show the rationality and the effectiveness of the method.

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47

Liputo, Cindy Regina, Masri Kudrat Umar, and Dewi Diana Paramata. "Evaluasi Pembelajaran Fisika Dasar Menggunakan Model Provus di Universitas." Ideas: Jurnal Pendidikan, Sosial, dan Budaya 9, no. 3 (August 31, 2023): 925. http://dx.doi.org/10.32884/ideas.v9i3.1163.

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There are various issues faced by college students while learning in college, which is why it is important to conduct evaluations to measure the success rate of the learning system. This research purposes to intended to analyze the problems encountered in learning so that solutions can be found, moreover the evaluation aims to assess the success of leraning that has been implemented. This research uses a qualitative and evaluative method of approach (discrepancy model's) with 4 evaluation dimensions (design, installation, process, and product). The result shows that overall, that design evaluation and installation evaluation are in the good category, process evaluation needs to be improved due to decreased attendance in the learning process, and product evaluation is considered good.

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48

Hutchins, M., K. Urama, E. Penning, J. Icke, C. Dilks, T. Bakken, C. Perrin, T. Saloranta, L. Candela, and J. Kämäri. "The model evaluation tool: guidance for applying benchmark criteria for models to be used in river basin management." River Systems 17, no. 1-2 (July 28, 2006): 23–48. http://dx.doi.org/10.1127/lr/17/2006/23.

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49

Kozłowski, Waldemar. "Model evaluation of infrastructure investments in municipal." Studia i Prace WNEiZ 43 (2016): 155–68. http://dx.doi.org/10.18276/sip.2016.43/1-14.

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50

Tong, Hengqing, Yichao Pan, Yang Ye, Shudan Lu, and Hengwen Liu. "Evaluation of Venture Capital Based on Evaluation Model." Technology and Investment 01, no. 03 (2010): 201–4. http://dx.doi.org/10.4236/ti.2010.13023.

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