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Factors Affecting Teacher Readiness for Online Learning (TROL) in Early Childhood Education: TISE and TPACK
This study aims to find empirical information about the effect of Technological Pedagogical Content Knowledge (TPACK), and Technology Integration Self Efficacy (TISE) on Teacher Readiness for Online Learning (TROL). This study uses a quantitative survey method with path analysis techniques. This study measures the readiness of kindergarten teachers in distance learning in Tanah Datar Regency, West Sumatra Province, Indonesia with a sampling technique using simple random sampling involving 105 teachers. Empirical findings reveal that; 1) there is a direct positive effect of Technology Integration Self Efficacy on Teacher Readiness for Online Learning; 2) there is a direct positive effect of PACK on Teacher Readiness for Online Learning; 3) there is a direct positive effect of Technology Integration Self Efficacy on TPACK. If want to improve teacher readiness for online learning, Technological Pedagogical Content Knowledge (TPACK) must be improved by paying attention to Technology Integration Self Efficacy (TISE).
Keywords: TROL, TPACK, TISE, Early Childhood Education
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Multimodal Sentiment Analysis of Instagram Using Cross-media Bag-of-words Model
Instagram, one of social media sharing services has increasing growth of use and popularity during recent years. Photos or videos shared by Instagram users are challenging to be mined and analyzed for some purposes. One type of studies can be applied to Instagram data is sentiment analysis, a field of study that learn and analyze people opinion, sentiment, and (or) evaluation about something. Sentiment analysis applied to Instagram can be used as analytics tool for some business purposes such as user behavior, market intelligence and user evaluation. This research aimed to analyze sentiment contained on Instagrams post by considering two modalities: images and English text on its caption. The Cross-media Bag-of-Words Model (CBM) was applied for analyzing the sentiment contained on Instagrams post. CBM treated text and image features as a unit of vector representation. 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An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics
[EN] The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056.GarcĂa, J.; Astorga, G.; Yepes, V. (2021). An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics. Mathematics. 9(3):1-20. https://doi.org/10.3390/math9030225S12093Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 10(12), 3445-3465. doi:10.1007/s13042-019-00931-8GarcĂa, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., ⊠Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. 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(2001). Probe selection algorithms with applications in the analysis of microbial communities. Bioinformatics, 17(Suppl 1), S39-S48. doi:10.1093/bioinformatics/17.suppl_1.s39Boros, E., Hammer, P. L., Ibaraki, T., & Kogan, A. (1997). Logical analysis of numerical data. Mathematical Programming, 79(1-3), 163-190. doi:10.1007/bf02614316Balas, E., & Carrera, M. C. (1996). A Dynamic Subgradient-Based Branch-and-Bound Procedure for Set Covering. Operations Research, 44(6), 875-890. doi:10.1287/opre.44.6.875Beasley, J. E. (1987). An algorithm for set covering problem. European Journal of Operational Research, 31(1), 85-93. doi:10.1016/0377-2217(87)90141-xBeasley, J. E. (1990). A lagrangian heuristic for set-covering problems. Naval Research Logistics, 37(1), 151-164. doi:10.1002/1520-6750(199002)37:13.0.co;2-2Beasley, J. ., & Chu, P. . (1996). A genetic algorithm for the set covering problem. 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Inspiring inclusion in your classroom and beyond
This article reflects upon teachersâ engagement in a Leadership for Inclusion
Community of Practice (LIn-CoP), which utilised the Participatory Action
Learning Action Research (PALAR) strategy. The study explored if and how
engagement could support teachers to develop and exercise leadership for
inclusion, using Grudnoff, Haigh, Cochran-Smith, Eil and Ludlow (2017) six
facets for equity. Data were drawn from seven early career elementary teachers in
the Republic of Ireland over a three-year period. The teachers sought successfully
to: 1) develop six facets of equity and, 2) overcome barriers to applying their
learning in their contexts. Analysis unveiled many examples of inclusive practices
for promoting equity, thus narrowing the values practice gap related to inclusion.
The findings also highlight for researchers and professional learning facilitators
the potential of the PALAR LIn-CoP model for applying teacher learning in
situated environments, in the face of organisational barriers
Selecting cash management models from a multiobjective perspective
[EN] This paper addresses the problem of selecting cash management models under different operating conditions from a multiobjective perspective considering not only cost but also risk. A number of models have been proposed to optimize corporate cash management policies. The impact on model performance of different operating conditions becomes an important issue. Here, we provide a range of visual and quantitative tools imported from Receiver Operating Characteristic (ROC) analysis. More precisely, we show the utility of ROC analysis from a triple perspective as a tool for: (1) showing model performance; (2) choosingmodels; and (3) assessing the impact of operating conditions on model performance. We illustrate the selection of cash management models by means of a numerical example.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; RodrĂguez-Aguilar, JA.; DĂaz-GarcĂa, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research. 261(1-2):275-288. https://doi.org/10.1007/s10479-017-2634-9S2752882611-2Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369â1382.Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. Berlin: Springer.Bi, J., & Bennett, K. P. (2003). Regression error characteristic curves. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 43â50.Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145â1159.da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In Decision models in engineering and management (pp. 11â28). New York: Springer.Doumpos, M., & Zopounidis, C. (2007). Model combination for credit risk assessment: A stacked generalization approach. Annals of Operations Research, 151(1), 289â306.Drummond, C., & Holte, R. C. (2000). Explicitly representing expected cost: An alternative to roc representation. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 98â207). New York: ACM.Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1), 95â130.Elkan, C. (2001). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, pp. 973â978). Lawrence Erlbaum associates Ltd.Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861â874.Flach, P. A. (2003). The geometry of roc space: understanding machine learning metrics through roc isometrics. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 194â201.Garcia-Bernabeu, A., Benito, A., Bravo, M., & Pla-Santamaria, D. (2016). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western spain. Annals of Operations Research, 245(1â2), 163â175.Glasserman, P. (2003). Monte Carlo methods in financial engineering (Vol. 53). New York: Springer.Gregory, G. (1976). Cash flow models: a review. Omega, 4(6), 643â656.HernĂĄndez-Orallo, J. (2013). Roc curves for regression. Pattern Recognition, 46(12), 3395â3411.HernĂĄndez-Orallo, J., Flach, P., & Ferri, C. (2013). Roc curves in cost space. Machine Learning, 93(1), 71â91.HernĂĄndez-Orallo, J., Lachiche, N., & Martınez-UsĂł, A. (2014). Predictive models for multidimensional data when the resolution context changes. In Workshop on learning over multiple contexts at ECML, volume 2014.Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine (Vol. 8, pp. 283â298). Amsterdam: Elsevier.Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Berlin: Springer.Ringuest, J. L. (2012). Multiobjective optimization: Behavioral and computational considerations. Berlin: Springer.Ross, S. A., Westerfield, R., & Jordan, B. D. (2002). Fundamentals of corporate finance (sixth ed.). New York: McGraw-Hill.Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, pp. 1â15.Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145â166.Steuer, R. E., Qi, Y., & Hirschberger, M. (2007). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297â317.Stone, B. K. (1972). The use of forecasts and smoothing in control limit models for cash management. Financial Management, 1(1), 72.Torgo, L. (2005). Regression error characteristic surfaces. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 697â702). ACM.Yu, P.-L. (1985). Multiple criteria decision making: concepts, techniques and extensions. New York: Plenum Press.Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill
Strategic Knowledge Measurement and Management
Knowledge and intellectual capital are now recognized as vital resources for organizational survival and competitive advantage. A vast array of knowledge measures has evolved, spanning many disciplines. This chapter reviews knowledge measures focusing on groups of individuals (such as teams, business and organizations), as they reflect the stock or flow of knowledge, as well as enabling processes that enhance knowledge stocks and flows. The chapter emphasizes the importance of organizational value chains, pivotal talent pools and the link between knowledge and competitive success, in understanding the significance of todayâs knowledge measures, and opportunities for future research and practice to enhance them
Smart street lighting system
A c, light pole, lamppost, street lamp, light standard or lamp standard is a raised source of light on the edge of a road or path. Street lighting provides a number of important benefits. It can be used to promote security in urban areas and to increase the quality of life by artificially extending the hours in which it is light so that activity can take place. Street lighting also improves safety for drivers, riders, and pedestrians..
Curiosity and its role in cross-cultural knowledge creation
This paper explores the role of curiosity in promoting cross-cultural knowledge creation
and competence development. It is based on a study with four international higher
educational institutions, all of which offer management and business education for local
and international students. The reality of multicultural and intercultural relationships is
researched using constructivist grounded theory method, with data collected through indepth
interviews, long-term observation and participation, and discussion of the social
reality as it was experienced by the participants. The study applies the concepts of
cultural knowledge development, cross-cultural competence and cultural distance. Based
on the comparative analysis, curiosity emerged as a personal condition conducive to the
cultural knowledge development process. The paper presents a cross-cultural
competence development process model, which takes into account the cultural curiosity
of the learners. The paper also provides tentative recommendations for the steps that
knowledge-creating multicultural organizations can take to develop cross-cultural
exchange, cultural knowledge creation and cross-cultural competence development.peer-reviewe
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