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Journal articles on the topic 'Empirical Evaluation'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Ma, Qing. "Empirical CALL evaluation." CALICO Journal 26, no. 1 (January 14, 2013): 108–22. http://dx.doi.org/10.1558/cj.v26i1.108-122.

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CALL evaluation is important because it is the most efficient means to prove CALL effectiveness. While both learning process and learning outcome should be investigated in empirical evaluation, the precise relationship between the two needs to be examined closely. Only by doing so can we identify useful CALL design features that facilitate relevant user-computer interaction which can lead to an improved learning outcome. This study examined how certain user actions can affect or predict receptive/productive vocabulary retention in a computer-assisted vocabulary learning (CAVL) program. Results showed that only the user actions which contributed to identifiable levels of vocabulary processing--those at which (a) the initial meaning-form mappings of the vocabulary items are established and (b) the vocabulary items are practiced in different contexts--significantly accounted for vocabulary retention. In addition, user actions accounted for receptive vocabulary retention better than for productive vocabulary retention.

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2

Thomsen, M., and L. Carlsen. "Evaluation of empirical versus non-empirical descriptors." SAR and QSAR in Environmental Research 13, no. 5 (January 2002): 525–40. http://dx.doi.org/10.1080/10629360290023359.

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3

Johnson, Nathan. "Empirical evaluation of monopolization." Critique 51, no. 1 (January 2, 2023): 51–70. http://dx.doi.org/10.1080/03017605.2023.2238448.

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4

Koubek, Tomáš, and David Procházka. "Empirical evaluation of augmented prototyping effectiveness." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 2 (2012): 143–50. http://dx.doi.org/10.11118/actaun201260020143.

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Augmented reality is a scientific field well known for more than twenty years. Although there is a huge number of projects that present promising results, the real usage of augmented reality applications for fulfilling common tasks is almost negligible. We believe that one of the principal reasons is insufficient usability of these applications. The situation is analogous to the desktop, mobile or cloud application development or even to the web pages design. The first phase of a technology adoption is the exploration of its potential. As soon as the technical problems are overcome and the technology is widely accepted, the usability is a principal issue. The usability is utmost important also from the business point of view. The cost of augmented reality implementation into the production process is substantial, therefore, the usability that is directly responsible for the implemented solution effectiveness must be appropriately tested. Consequently, the benefit of the implemented solution can be measured.This article briefly outlines common techniques used for usability evaluation. Discussed techniques were designed especially for evaluation of desktop applications, mobile solutions and web pages. In spite of this drawback, their application on augmented reality products is usually possible. Further, a review of existing augmented reality project evaluations is presented.Based on this review, a usability evaluation method for our augmented prototyping application is proposed. This method must overcome the fact that the design is a creative process. Therefore, it is not possible to take into account common criteria such as time consumption.

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5

Raimondo, Estelle. "The power and dysfunctions of evaluation systems in international organizations." Evaluation 24, no. 1 (January 2018): 26–41. http://dx.doi.org/10.1177/1356389017749068.

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Evaluations do not take place in a vacuum. Evaluation systems are embedded within organizations; they shape and are shaped by organizational norms, processes, and behaviors. In International Organizations, evaluation systems are ubiquitous. Yet, little is known about how they “function,” namely how they are used, how they contribute to organizational performance, and how they influence actors’ behaviors. These are empirical questions that cannot be solved without a robust theoretical grounding, which is currently absent from the existing evaluation literature. This article seeks to bridge some of the identified gaps by weaving together insights from evaluation theory and international organization sociology into a unifying framework of factors. The article then demonstrates how the framework can be used to empirically study the relative power and dysfunction of evaluation systems within International Organizations. A forthcoming connected contribution will illustrate such empirical inquiry through the case of the World Bank’s project-level evaluation system.

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6

Raby, Caroline, Dean Alexis, Anthony Dickinson, and Nicola Clayton. "Empirical evaluation of mental time travel." Behavioral and Brain Sciences 30, no. 3 (June 2007): 330–31. http://dx.doi.org/10.1017/s0140525x07002166.

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AbstractAlthough the mental time travel (MTT) hypothesis provides a rich, conceptual framework, the absence of clear, empirically tractable, behavioural criteria for determining the capacity for MTT restricts its usefulness in comparative research. Examples of empirical criteria for evaluating MTT in animals are given. We also question the authors' evaluation of semantic foresight and their even-handedness in assessing human and nonhuman behaviour.

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7

Vial, Daniel, and Vijay Subramanian. "Empirical Policy Evaluation With Supergraphs." IEEE Journal on Selected Areas in Information Theory 2, no. 2 (June 2021): 641–51. http://dx.doi.org/10.1109/jsait.2021.3073257.

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8

McGill, Brian J., Brian A. Maurer, and Michael D. Weiser. "EMPIRICAL EVALUATION OF NEUTRAL THEORY." Ecology 87, no. 6 (June 2006): 1411–23. http://dx.doi.org/10.1890/0012-9658(2006)87[1411:eeont]2.0.co;2.

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9

Pagel, Mark D., and Clifford E. Lunneborg. "Empirical evaluation of ridge regression." Psychological Bulletin 97, no. 2 (1985): 342–55. http://dx.doi.org/10.1037/0033-2909.97.2.342.

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10

Riding, T. M. "Exploring values: an empirical evaluation." Journal of Learning Disabilities for Nursing, Health, and Social Care 1, no. 1 (March 1997): 25–30. http://dx.doi.org/10.1177/146900479700100105.

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11

Krivoruchko, Konstantin, and Alexander Gribov. "Evaluation of empirical Bayesian kriging." Spatial Statistics 32 (August 2019): 100368. http://dx.doi.org/10.1016/j.spasta.2019.100368.

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12

Komorowski, Ludwik. "Empirical evaluation of chemical hardness." Chemical Physics Letters 134, no. 6 (March 1987): 536–40. http://dx.doi.org/10.1016/0009-2614(87)87188-9.

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13

Flach, Peter. "Empirical Evaluation of Predictive Models." ACM SIGIR Forum 56, no. 1 (June 2022): 1–5. http://dx.doi.org/10.1145/3582524.3582528.

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I give a brief overview of my recent keynote at the 2022 European Conference on Information Retrieval that was held in Stavanger, Norway. I pay particular attention to some basic questions involving the F-score that appear to lead to confusion. I also settle a question raised at the conference by reconstructing an account from Van Rijsbergen's classic text Information Retrieval.

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14

Christie, Christina A. "Advancing Empirical Scholarship to Further Develop Evaluation Theory and Practice." Canadian Journal of Program Evaluation 26, no. 1 (March 2011): 1–18. http://dx.doi.org/10.3138/cjpe.026.001.

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Abstract: Good theory development is grounded in empirical inquiry. In the context of educational evaluation, the development of empirically grounded theory has important benefits for the field and the practitioner. In particular, a shift to empirically derived theory will assist in advancing more systematic and contextually relevant evaluation practice, as well as lead to the development of contingency theories that specify the conditions under which particular evaluation practices are optimal. In contrast to the historical outside-in stance, empirical research on the field of evaluation must acknowledge practitioners as “knowers,” allowing for unique insights into the intersection of theory and practice.

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15

Patiño, David, and Francisco Gómez-García. "Do Quasi-Hyperbolic Preferences Explain Academic Procrastination? An Empirical Evaluation." Revista Hacienda Pública Española 230, no. 3 (September 2019): 95–124. http://dx.doi.org/10.7866/hpe-rpe.19.3.4.

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16

Saranya, R. Kamala, and Dr S. Duraisamy Dr. S. Duraisamy. "An Empirical Analysis on Training & Development Evaluation Across Organizations." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 262–63. http://dx.doi.org/10.15373/22778179/apr2014/91.

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17

Lozano Povis, Arlitt Amy, and Elías Adrián Sanabria Perez. "Experimental Evaluation of an Empirical Equation in a Gaseous Flow." Chemistry & Chemical Technology 18, no. 1 (March 25, 2024): 57–65. http://dx.doi.org/10.23939/chcht18.01.057.

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In this paper, the estimation error of Dr. Pole's empirical equation was evaluated using copper pipes of different diameters (0.00953, 0.0127, 0.01588 m), under different flow pressure conditions (0, 300, 500, 1000, 1500, 2000, 2500, 3000 L/h). To carry out the experiments, the following instruments were used: an air compressor, 2 flow valves, a needle valve, a gas rotameter, copper piping, pressure gauges and transmitters, a Norus data logger with 4 to 20 mA output signals, thermocouples, and thermoresistors. They allow us to establish that the air pressure drops when the flowing through the pipes is higher (380 Pa) for small diameter pipes (0.00953 m), compared to larger diameters (0.01270 m and 0.01588 m) with a maximum of 54 and 28 Pa, respectively; and in relation to the flow rates, the pressure drop increases with a quadratic trend with respect to the flow rate. Finally, the residual errors that the empirical equation has in the pressure drop calculations, in general terms, are not of great magnitude.

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18

Koutsabasis, Panayiotis. "Empirical Evaluations of Interactive Systems in Cultural Heritage." International Journal of Computational Methods in Heritage Science 1, no. 1 (January 2017): 100–122. http://dx.doi.org/10.4018/ijcmhs.2017010107.

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An increasing number of interactive systems aim to enhance the user experience (UX) of visitors at museums, archaeological places and cultural sites. This paper presents a review of empirical evaluations of interactive systems in cultural heritage (CH) based on a systematically selected sample of 53 publications from 2012-2016. Empirical evaluations examine the degree an interactive system satisfies user goals and expectations and are inherently complex activities that require careful planning and selection of methods. The review reports on (a) interactive systems of CH in terms of: purpose, technology, cultural content and location of interaction and (b) methods of empirical evaluation in terms of: dimensions of UX, general approach, data collection and participants. The paper provides discussion, critique and outlook on issues including: extending the evaluation dimensions towards the cultural value and the essence of interactivity; placing more consideration on CH professionals; identifying new evaluation methods that balance qualitative results to evidence-based approaches.

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19

Zugal, Stefan, Cornelia Haisjackl, Jakob Pinggera, and Barbara Weber. "Empirical Evaluation of Test Driven Modeling." International Journal of Information System Modeling and Design 4, no. 2 (April 2013): 23–43. http://dx.doi.org/10.4018/jismd.2013040102.

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Declarative approaches to process modeling are regarded well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative process models impede their usage. To compensate for these shortcomings, Test Driven Modeling (TDM) has been proposed. This paper reports on an empirical investigation in which TDM is viewed from two different angles. First, the impact of TDM on communication is explored in a case study. Results indicate that domain experts are inclined to use test cases for communicating with the model builder (system analyst) and prefer them over the process model. The second part of the investigation, a controlled experiment, investigates the impact of TDM on process model maintenance. Data gathered in this experiment indicates that the adoption of test cases significantly lowers cognitive load and increases the perceived quality of changes.

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20

Fetais, Noora, and Peter Cheng. "Empirical evaluation of constraint diagrams notation." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): CSPS13. http://dx.doi.org/10.5339/qfarf.2012.csps13.

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21

Handzic, Meliha, and Joon Ho-Hur. "Corporate E-Learning: An Empirical Evaluation." Journal of Information & Knowledge Management 04, no. 04 (December 2005): 229–35. http://dx.doi.org/10.1142/s0219649205001195.

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This paper explores user views of the current e-learning practices in an organisational setting. Eighty employees of Korean Air, who were enrolled in an e-learning course, participated in the study on a voluntary basis. Data on employees' perceptions of importance and satisfaction with their course portal were gathered by administering a survey questionnaire. The study revealed that employees considered all portal features as quite important and satisfying, although some more than others. They also agreed that there was scope for further improvement through adding extra functionality. These findings have important implications for improving the effectiveness of corporate e-learning.

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22

Hedbring, Charles. "Empirical Software Evaluation: A Practical Alternative." Australian Journal of Human Communication Disorders 15, no. 2 (December 1987): 75–88. http://dx.doi.org/10.3109/asl2.1987.15.issue-2.06.

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23

Selby, R. W., V. R. Basili, and F. T. Baker. "Cleanroom Software Development: An Empirical Evaluation." IEEE Transactions on Software Engineering SE-13, no. 9 (September 1987): 1027–37. http://dx.doi.org/10.1109/tse.1987.233525.

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24

Phillips, P. J., and K. W. Bowyer. "Empirical Evaluation of Computer Vision Algorithms." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 4 (April 1999): 289–90. http://dx.doi.org/10.1109/tpami.1999.761260.

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25

Hao Wu, Aswin C. Sankaranarayanan, and Rama Chellappa. "Online Empirical Evaluation of Tracking Algorithms." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 8 (August 2010): 1443–58. http://dx.doi.org/10.1109/tpami.2009.135.

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26

Thoemmes, Felix. "Empirical evaluation of directional-dependence tests." International Journal of Behavioral Development 39, no. 6 (April 27, 2015): 560–69. http://dx.doi.org/10.1177/0165025415582055.

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Testing of directional dependence is a method to infer causal direction that recently has attracted some attention. Previous examples by e.g. von Eye and DeShon (2012a) and extensive simulation studies by Pornprasertmanit and Little (2012) have demonstrated that under specific assumptions, directional-dependence tests can recover the true causal direction between two variables. Simulation results are important in the evaluation of any statistical method, but they are necessarily less complex than real data that come with potential irregularities (e.g. departures from linearity, presence of confounders, etc.). In this article, we evaluate the performance of directional-dependence tests using benchmark data consisting of 65 variable pairs with known causal order. We find that between 21% and 43% of all cases are correctly classified using different directional-dependence tests that rely on differences in skew, kurtosis, or a combined measure. We then examine some of the assumptions of the directional-dependence test and find that for virtually all variable pairs, some assumptions are violated. When only pairs in which assumptions are fulfilled are selected, performance of all directional-dependence tests improves. We probe whether particular features of the variable pairs impact whether a test yields a correct or incorrect result, but find no strong predictors. Our findings provide a complimentary picture to previously conducted simulation studies, and highlight the fact that directional-dependence tests are prone to causal classification errors when key assumptions are violated. Such violations are potentially common in real data.

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27

WEISNER, CONSTANCE. "INNOVATIVE POLICY STRATEGIES NEED EMPIRICAL EVALUATION." Addiction 106, no. 12 (November 3, 2011): 2069–70. http://dx.doi.org/10.1111/j.1360-0443.2011.03562.x.

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28

Cheatham, James M. "The Empirical Evaluation of Clinical Practice:." Journal of Social Service Research 10, no. 2-4 (December 16, 1987): 163–77. http://dx.doi.org/10.1300/j079v10n02_10.

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29

Cousins, J. Bradley, and Kenneth A. Leithwood. "Current Empirical Research on Evaluation Utilization." Review of Educational Research 56, no. 3 (September 1986): 331–64. http://dx.doi.org/10.3102/00346543056003331.

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This paper reviews empirical research conducted during the past 15 years on the use of evaluation results. Sixty-five studies in education, mental health, and social services are described in terms of their methodological characteristics, their orientation toward dependent and independent variables, and the relationships between such variables. A conceptual framework is developed that lists 12 factors that influence use; six of these factors are associated with characteristics of evaluation implementation and six with characteristics of decision or policy setting. The factors are discussed in terms of their influence on evaluation utilization, and their relative influence on various types of use is compared. The paper concludes with a statement about implications for research and practice.

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30

Sherman, Douglas J. "Empirical Evaluation of Longshore-Current Models." Geographical Review 78, no. 2 (April 1988): 158. http://dx.doi.org/10.2307/214174.

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31

Wang, Zhemin. "An Empirical Evaluation Of Goodwill Accounting." Journal of Applied Business Research (JABR) 9, no. 4 (September 27, 2011): 127. http://dx.doi.org/10.19030/jabr.v9i4.6003.

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While the current amortization period for goodwill is criticized by many financial statement preparers for being too short, the accounting profession and regulatory bodies indicate the intention of further reducing the amortization period. This study documents consistent evidence suggesting that any attempt to significantly shorten the amortization period may impose an unfair penalty on firms with goodwill assets and cause the reported goodwill to be significantly understated.

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32

Feng, Wei, Miguel Figliozzi, and Robert L. Bertini. "Empirical Evaluation of Transit Signal Priority." Transportation Research Record: Journal of the Transportation Research Board 2488, no. 1 (January 2015): 20–31. http://dx.doi.org/10.3141/2488-03.

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33

Offutt, A. J., and S. D. Lee. "An empirical evaluation of weak mutation." IEEE Transactions on Software Engineering 20, no. 5 (May 1994): 337–44. http://dx.doi.org/10.1109/32.286422.

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34

Kadipasaoglu, Sükran N., Julio L. Peixoto, and Basheer M. Khumawala. "Global manufacturing practices: an empirical evaluation." Industrial Management & Data Systems 99, no. 3 (May 1999): 101–8. http://dx.doi.org/10.1108/02635579910370652.

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35

Whitaker, Stephan, and Thomas J. Fitzpatrick. "LAND BANK 2.0: AN EMPIRICAL EVALUATION." Journal of Regional Science 56, no. 1 (August 25, 2015): 156–75. http://dx.doi.org/10.1111/jors.12206.

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36

Arpaci, Remzi H., David E. Culler, Arvind Krishnamurthy, Steve G. Steinberg, and Katherine Yelick. "Empirical evaluation of the CRAY-T3D." ACM SIGARCH Computer Architecture News 23, no. 2 (May 1995): 320–31. http://dx.doi.org/10.1145/225830.224443.

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37

Hafner, A. N. "Empirical Testing: The Prototypers Evaluation Mechanism." Naval Engineers Journal 109, no. 1 (January 1997): 41–46. http://dx.doi.org/10.1111/j.1559-3584.1997.tb01929.x.

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38

Fernández-Garcı́a, N. L., R. Medina-Carnicer, A. Carmona-Poyato, F. J. Madrid-Cuevas, and M. Prieto-Villegas. "Characterization of empirical discrepancy evaluation measures." Pattern Recognition Letters 25, no. 1 (January 2004): 35–47. http://dx.doi.org/10.1016/j.patrec.2003.08.011.

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39

McCarthy, Philip M., and Scott Jarvis. "vocd: A theoretical and empirical evaluation." Language Testing 24, no. 4 (October 2007): 459–88. http://dx.doi.org/10.1177/0265532207080767.

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40

Boyle, Craig, Swee Hor Teh, and Clay Williams. "An Empirical Evaluation of Hypertext Interfaces." Hypermedia 2, no. 3 (January 1990): 235–48. http://dx.doi.org/10.1080/09558543.1990.12031185.

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41

Hanks, Brian. "Empirical evaluation of distributed pair programming." International Journal of Human-Computer Studies 66, no. 7 (July 2008): 530–44. http://dx.doi.org/10.1016/j.ijhcs.2007.10.003.

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42

García-Peñalvo, Francisco José, Lourdes Moreno López, and Mª Cruz Sánchez-Gómez. "Empirical evaluation of educational interactive systems." Quality & Quantity 52, no. 6 (August 13, 2018): 2427–34. http://dx.doi.org/10.1007/s11135-018-0808-4.

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43

Sawada, Yasuyuki. "The immiserizing growth: an empirical evaluation." Applied Economics 41, no. 13 (May 2009): 1613–20. http://dx.doi.org/10.1080/00036840802052099.

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44

Rozell, Elizabeth J., Charles E. Pettijohn, and R. Stephen Parker. "An empirical evaluation of emotional intelligence." Journal of Management Development 21, no. 4 (May 2002): 272–89. http://dx.doi.org/10.1108/02621710210430272.

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45

Pauley, Wayne. "Cloud Provider Transparency: An Empirical Evaluation." IEEE Security & Privacy Magazine 8, no. 6 (November 2010): 32–39. http://dx.doi.org/10.1109/msp.2010.140.

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46

Manegold, Stefan. "An empirical evaluation of XQuery processors." Information Systems 33, no. 2 (April 2008): 203–20. http://dx.doi.org/10.1016/j.is.2007.05.004.

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47

Khari, Manju, and Prabhat Kumar. "Empirical Evaluation of Hill Climbing Algorithm." International Journal of Applied Metaheuristic Computing 8, no. 4 (October 2017): 27–40. http://dx.doi.org/10.4018/ijamc.2017100102.

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The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

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48

Cleland, Timothy P., Kristyn Voegele, and Mary H. Schweitzer. "Empirical Evaluation of Bone Extraction Protocols." PLoS ONE 7, no. 2 (February 14, 2012): e31443. http://dx.doi.org/10.1371/journal.pone.0031443.

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49

Bayer, Betty M., James L. Bonta, and Laurence L. Motiuk. "The PD subscales: An empirical evaluation." Journal of Clinical Psychology 41, no. 6 (November 1985): 780–88. http://dx.doi.org/10.1002/1097-4679(198511)41:6<780::aid-jclp2270410610>3.0.co;2-b.

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50

A., Anto Shaju Sathish. "Evaluation of Unorganized Money Market in Rural Goa – An Empirical Study." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 105–10. http://dx.doi.org/10.5373/jardcs/v12sp3/20201243.

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