Tesis sobre el tema "Prediction Explanation"
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Gordon, Richard Douglas. "Explanation and prediction in the labour process theory". Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30583.
Texto completoArts, Faculty of
Philosophy, Department of
Graduate
Bonawitz, Elizabeth R. (Elizabeth Robbin). "The rational child : theories and evidence in prediction, exploration, and explanation". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47891.
Texto completoIncludes bibliographical references (p. 122-133).
In this thesis, rational Bayesian models and the Theory-theory are bridged to explore ways in which children can be described as Bayesian scientists. I investigate what it means for children to take a rational approach to processes that support learning. In particular, I present empirical studies that show children making rational predictions, exploration, and explanations. I test the claim that differences in prior beliefs or changes in the observed evidence should affect these behaviors. The studies presented in this thesis encompass two manipulations: in some conditions, children's prior beliefs are equal, but the patterns of evidence are varied; in other conditions, children observe identical evidence but children's prior beliefs are varied. I incorporate an additional approach in this thesis, testing children within a variety of domains, tapping into their intuitive theories of biological kinds, psychosomatic illness, balance, and physical systems. Chapter One introduces the problem. Chapter Two explores how evidence and children's strong beliefs about biological events and psychosomatic illness influence their forced-choice explanations in a story-book task. Chapter Three presents a training study to further investigate the developmental differences discussed in Chapter Two. Chapter Four looks at how children's strong differential beliefs of balance interact with evidence to affect their predictions, play, explanations, and learning.
(cont.) Chapter Five looks at children's exploratory play with a jack-in-the-box, (where children don't have strong, differential beliefs), given different patterns of evidence. Chapter Six investigates children's explanations following theory-neutral evidence about a mechanical toy. Chapter Seven concludes the thesis. The following chapters will suggest that frameworks combining evidence and theories capture children's causal learning about the world.
by Elizabeth R. Bonawitz.
Ph.D.
Watson, Jason Paul 1971. "Explanation and prediction of curious experimental phenomena in lasers and nonlinear optics". Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/282875.
Texto completoHaar, D. H. "Formalised modelling of action theory in the explanation of crime for prediction, deduction and intervention". Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599815.
Texto completoMcKay, William L. "Hope and suicide resilience in the prediction and explanation of suicidality experiences in university students". Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1456285751&sid=3&Fmt=2&clientId=18949&RQT=309&VName=PQD.
Texto completoOlofsson, Nina. "A Machine Learning Ensemble Approach to Churn Prediction : Developing and Comparing Local Explanation Models on Top of a Black-Box Classifier". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210565.
Texto completoMetoder för att prediktera utträde är vanliga inom Customer Relationship Management och har visat sig vara värdefulla när det kommer till att behålla kunder. För att kunna prediktera utträde med så hög säkerhet som möjligt har den senasteforskningen fokuserat på alltmer komplexa maskininlärningsmodeller, såsom ensembler och hybridmodeller. En konsekvens av att ha alltmer komplexa modellerär dock att det blir svårare och svårare att förstå hur en viss modell har kommitfram till ett visst beslut. Tidigare studier inom maskininlärningsinterpretering har haft ett globalt perspektiv för att förklara svårförståeliga modeller. Denna studieutforskar lokala förklaringsmodeller för att förklara individuella beslut av en ensemblemodell känd som 'Random Forest'. Prediktionen av utträde studeras påanvändarna av Tink – en finansapp. Syftet med denna studie är att ta lokala förklaringsmodeller ett steg längre genomatt göra jämförelser av indikatorer för utträde mellan olika användargrupper. Totalt undersöktes tre par av grupper som påvisade skillnader i tre olika variabler. Sedan användes lokala förklaringsmodeller till att beräkna hur viktiga alla globaltfunna indikatorer för utträde var för respektive grupp. Resultaten visade att detinte fanns några signifikanta skillnader mellan grupperna gällande huvudindikatorerna för utträde. Istället visade resultaten skillnader i mindre viktiga indikatorer som hade att göra med den typ av information som lagras av användarna i appen. Förutom att undersöka skillnader i indikatorer för utträde resulterade dennastudie i en välfungerande modell för att prediktera utträde med förmågan attförklara individuella beslut. Random Forest-modellen visade sig vara signifikantbättre än ett antal enklare modeller, med ett AUC-värde på 0.93.
Hasan, Rakebul. "Prédire les performances des requêtes et expliquer les résultats pour assister la consommation de données liées". Thesis, Nice, 2014. http://www.theses.fr/2014NICE4082/document.
Texto completoOur goal is to assist users in understanding SPARQL query performance, query results, and derivations on Linked Data. To help users in understanding query performance, we provide query performance predictions based on the query execution history. We present a machine learning approach to predict query performances. We do not use statistics about the underlying data for our predictions. This makes our approach suitable for the Linked Data scenario where statistics about the underlying data is often missing such as when the data is controlled by external parties. To help users in understanding query results, we provide provenance-based query result explanations. We present a non-annotation-based approach to generate why-provenance for SPARQL query results. Our approach does not require any re-engineering of the query processor, the data model, or the query language. We use the existing SPARQL 1.1 constructs to generate provenance by querying the data. This makes our approach suitable for Linked Data. We also present a user study to examine the impact of query result explanations. Finally to help users in understanding derivations on Linked Data, we introduce the concept of Linked Explanations. We publish explanation metadata as Linked Data. This allows explaining derived data in Linked Data by following the links of the data used in the derivation and the links of their explanation metadata. We present an extension of the W3C PROV ontology to describe explanation metadata. We also present an approach to summarize these explanations to help users filter information in the explanation, and have an understanding of what important information was used in the derivation
Rawstorne, Patrick. "A systematic analysis of the theory of reasoned action, the theory of planned behaviour and the technology acceptance model when applied to the prediction and explanation of information systems use in mandatory usage contexts". Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060815.154410/index.html.
Texto completoBantegnie, Brice. "Eliminating propositional attitudes concepts". Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0020.
Texto completoIn this dissertation, I argue for the elimination of propositional attitudes concepts. In the first chapter I sketch the landscape of eliminativism in contemporary philosophy of mind and cognitive science. There are two kinds of eliminativism: eliminative materialism and concept eliminativism. One can further distinguish between folk and science eliminativism about concepts: whereas the former says that the concept should be eliminated from our folk theories, the latter says that the concept should be eliminated form our scientific theories. The eliminativism about propositional attitudes concepts I defend is a species of the latter. In the next three chapters I put forward three arguments for this thesis. I first argue that the interventionist theory of causation cannot lend credit to our claims of mental causation. I then support the thesis by showing that propositional attitudes concepts aren't natural kind concepts because they cross-cut the states of the modules posited by the thesis of massive modularity, a thesis which, I contend, is part of our best research-program. Finally, my third argument rests on science eliminativism about the concept of mental content. In the two last chapters of the dissertation I first defend the elimination of the concept of mental content from the success argument, according to which as psychologists produce successful science while using the concept of mental content, the concept should be conserved. Then, I dismiss an alternative way of eliminating the concept, that is, the way taken by proponents of extended cognition, by refuting what I take to be the best argument for extended cognition, namely, the system argument
Lonjarret, Corentin. "Sequential recommendation and explanations". Thesis, Lyon, 2021. http://theses.insa-lyon.fr/publication/2021LYSEI003/these.pdf.
Texto completoRecommender systems have received a lot of attention over the past decades with the proposal of many models that take advantage of the most advanced models of Deep Learning and Machine Learning. With the automation of the collect of user actions such as purchasing of items, watching movies, clicking on hyperlinks, the data available for recommender systems is becoming more and more abundant. These data, called implicit feedback, keeps the sequential order of actions. It is in this context that sequence-aware recommender systems have emerged. Their goal is to combine user preference (long-term users' profiles) and sequential dynamics (short-term tendencies) in order to recommend next actions to a user. In this thesis, we investigate sequential recommendation that aims to predict the user's next item/action from implicit feedback. Our main contribution is REBUS, a new metric embedding model, where only items are projected to integrate and unify user preferences and sequential dynamics. To capture sequential dynamics, REBUS uses frequent sequences in order to provide personalized order Markov chains. We have carried out extensive experiments and demonstrate that our method outperforms state-of-the-art models, especially on sparse datasets. Moreover we share our experience on the implementation and the integration of REBUS in myCADservices, a collaborative platform of the French company Visiativ. We also propose methods to explain the recommendations provided by recommender systems in the research line of explainable AI that has received a lot of attention recently. Despite the ubiquity of recommender systems only few researchers have attempted to explain the recommendations according to user input. However, being able to explain a recommendation would help increase the confidence that a user can have in a recommendation system. Hence, we propose a method based on subgroup discovery that provides interpretable explanations of a recommendation for models that use implicit feedback
Löfström, Helena. "Time to Open the Black Box : Explaining the Predictions of Text Classification". Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-14194.
Texto completoReitzel-Jaffe, Deborah D. "Predicting relationship abuse, a structural equation model analysis of a social learning explanation". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ28518.pdf.
Texto completoMcGrath, Shelly Ann. "Structural Explanations, Need, Or Rurality: Predicting Availability Of Domestic Violence Emergency Services". OpenSIUC, 2009. https://opensiuc.lib.siu.edu/dissertations/53.
Texto completoMcGrath, Shelly A. "Structural explanations, need, or rurality : predicting the availability of domestic violence emergency services /". Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1879034001&sid=17&Fmt=2&clientId=1509&RQT=309&VName=PQD.
Texto completo"Department of Sociology." Keywords: Domestic violence, Emergency services, Rural. Includes bibliographical references (p. 202-217). Also available online.
Lattouf, Mouzeina. "Assessment of Predictive Models for Improving Default Settings in Streaming Services". Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284482.
Texto completoStreamingtjänster erbjuder olika inställningar där kunderna kan välja ljud- och videokvalitet baserat på personliga preferenser. Majoriteten av användarna gör aldrig ett aktivt val; de tilldelas istället en standardkvalitetsinställning som väljs automatiskt baserat på vissa parametrar, som internetanslutningskvalitet. Denna avhandling undersöker anpassning av standardljudinställningen, med avsikt att förbättra användarupplevelsen. Detta uppnås genom att tillämpa maskininlärning på den andel användare som har aktivt ändrat kvalitetsinställningen. Antagandet att användarlikhet hos användare som gör ett aktivt val kan utnyttjas för att påverka användarupplevelsen var tanken bakom detta examensarbete. Det utfärdades för att studera vilken typ av data från olika kategorier: demografi, produkt och konsumtion är mest förutsägande för användarens smak i ljudkvalitet. En fallstudie genomfördes för att uppnå målen för denna avhandling. Fem prediktiva modellprototyper tränades, utvärderades, jämfördes och analyserades med två olika algoritmer: XGBoost och Logistisk Regression, och inriktade på två regioner: Sverige och Brasilien. Analys av funktionsvikt genomfördes med SHapley Additive exPlanations (SHAP), en enhetlig ram för att tolka förutsägelser med en spelteoretisk metod, och genom att mäta koefficientvikter för att bestämma de mest prediktiva funktionerna. Förutom att utforska funktionens påverkan, svarar avhandlingen också på hur rimligt det är att generalisera dessa modeller för icke-selektiva användare genom att utföra hypotesprövning. Projektet omfattade också biasanalys mellan användare med och utan aktiva kvalitetsinställningar och hur det påverkar modellerna. Modellerna med XGBoost hade högre prestanda. Resultaten visade att demografisk data och produktdata hade en högre inverkan på modellförutsägelser i båda regionerna. Däremot hade olika regioner inte samma datafunktioner som mest prediktiva, skillnader observerades i funktionsvikt mellan regioner och även mellan plattformar. Resultaten av hypotesprövningen indikerade inte på vägande anledning för att anse att modellerna skulle fungera för icke-selektiva användare. Däremot har metoden påverkats negativt av andra faktorer som små förändringar i stora datamängder som påverkar den statistiska signifikansen. Data bias hittades i vissa datafunktioner, vilket indikerade en korrelation men inte orsaken bakom mönstren. Resultaten av denna avhandling visar dessutom hur maskininlärning kan förbättra användarupplevelsen när det gäller standardinställningar för ljudkvalitet, genom att utnyttja modeller för användarlikhet hos användare som har ändrat ljudkvaliteten till det mest lämpliga för dem.
Hedström, Anna. "Explainable Artificial Intelligence : How to Evaluate Explanations of Deep Neural Network Predictions using the Continuity Test". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281279.
Texto completoMed ett ökat intresse att använda djupa neurala nätverk i olika delar av samhället, ställs nya krav på dess tolkbarhet. Förnyat forskningsintresse har således infunnit sig för att utveckla förklaringsmodeller som kan redogöra varför ett beslut har tagits av en algoritm. Förklaringsmodellerna har dock visat sig vara långt ifrån felfria och frågan om hur man utvärderar dem har i stort sett förblivit obesvarad. I detta arbete har vi undersökt hur populära förklaringsmodeller evalueras med ett specifikt fokus på en evalueringsteknik som har visat sig vara särskilt problematisk - “kontinuitetstestet”. Testet fångar upp förklaringarnas “sensitivitet” genom att mäta hur en förklaring, i förhållande till modellens prediktion, förändras när dess input modifieras. Även om det kan låta som en rimlig förväntning att förklaringsmetoderna skall uppträda konsekvent i sin inputdomän, så visar vi i bildklassificeringsexperiment på både syntetisk och real data, att det empiriska sambandet mellan hur förklaringar och nätverk svarar på modifierad input är obetydlig. Därmed utmanar vi idén om att förklaringar och modellresultat kan och bör jämföras. Som en andra inriktning i detta arbete påpekar vi också hur och varför det är problematiskt att vanligt förekommande bildmanipuleringstekniker tenderar att producera bilder som ligger långt ifrån den ursprungliga datadistributionen. I strävan efter mer trovärdiga och bättre bildmanipuleringstekniker för kontinuitetstestet, presenterar vi därför ett alternativt tillvägagångssätt som förlitar sig på sampling i det latenta rummet, producerat av en probabilistisk generativ modell. För detta ändamål hoppas vi därför att arbetet som presenteras i denna examensarbete inte bara belyser brister i aktuella utvärderingstekniker, utan också bidrar med idéer om hur man kan förbättra dem.
Walsh, Emma. "Predicting the impact of health states on well-being : explanations and remedies for biased judgments". Thesis, City, University of London, 2009. http://openaccess.city.ac.uk/18257/.
Texto completoSu, Susan Chih-Wen. "Female property crime offenders: Explanations from economic marginalization perspective". CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2673.
Texto completo"Harnessing Teamwork in Networks: Prediction, Optimization, and Explanation". Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.51637.
Texto completoDissertation/Thesis
Doctoral Dissertation Computer Science 2018
Cornelius, Chelsea Ann. "Development of beliefs about chance and luck". Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4900.
Texto completotext
HE, XUE-FENG y 何雪鳳. "An Application of the Prediction- Observation- Explanation Strategy to the Biochar Subject". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/80444784887912805473.
Texto completo環球科技大學
觀光與生態旅遊系環境資源管理碩士班
104
This research uses Quasi-experimental methods to execute a biochar course for fifth grade elementary school students by experimental activities and assessment of POE (Prediction- Observation – Explanation). The aim of this research is to investigate the learning effect of the fifth grade students after the biochar teaching activity. The Researcher directed an unequal post-test control group designed for two classes in a medium-size elementary school in Chiayi County. Class A is the experimental group and class B is the control group. Based on teaching objectives and research purposes, the researcher designed the course according to the students’ biochar learning goal and of their applying POE into biochar teaching. The data sources include before and after measured analysis, the POE learning sheet, and two-tier diagnostic test data. According to the analysis data, the researcher comes to the following conclusions: A. Compared to traditional didactic teaching, POE teaching establishes the concept of biochar more among the high-score students studied in this science. B. Adopted two-tier diagnostic tests can filter and inspect whether students sank into myths or mistakes. Except for using POE teaching for the high-score students studied in science, the results of traditional didactic teaching or POE teaching for general students shows obvious discrepancy in the post test which uses two-tier diagnostic tests, and these students may need remedial teaching for myths someday C. During the biochar classes, if a teacher doesn’t timely clarify a student's previous myths when the teaching involves the original knowledge, it may obstruct a students’ learning of new concepts.
Stockwell, David. "Machine learning and the problem of prediction and explanation in ecological modelling". Phd thesis, 1992. http://hdl.handle.net/1885/139553.
Texto completoYang, Hui-Ting y 楊惠婷. "Facilitating Preschoolers’ Conceptual Understanding about Gears: The Effect of the Prediction-Observation-Explanation(POE)Strategy". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8whqx2.
Texto completo國立屏東科技大學
幼兒保育系所
105
Many studies have found that playing building blocks can help to improve problem-solving skills and promote knowledge building. This study was conducted to examine the effects of the building blocks course embedded the prediction, observation, and explanation model (POE) on facilitating the preschoolers’ acquisition of gear concepts. The preschoolers’ alternative conceptions of gears after the treatments were also investigated. 60 preschoolers were recruited and randomly assigned into either an experimental group or a control group. The former received the building block course with the POE model embedded, the latter received the base course. The measurement included the pretest, posttest, and two-week delayed test. The course included four one-hour treatments and each participant was instructed individually during the treatment and the tests. The findings indicated that integrating the POE model into the building block course could effectively enhance the preschoolers’ acquisition of gears concepts. However, some alternative conceptions were identified after the treatments, such as “Two gears with different sizes interlock. The bigger the gear, the faster its turning speed.
Kuo, Chia-Yu y 郭家諭. "Explainable Risk Prediction System for Child Abuse Event by Individual Feature Attribution and Counterfactual Explanation". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/yp2nr3.
Texto completo國立交通大學
統計學研究所
107
There always have a trade-off: Performance or Interpretability. The complex model, such as ensemble learning can achieve outstanding prediction accuracy. However, it is not easy to interpret the complex model. Understanding why a model made a prediction help us to trust the black-box model, and also help users to make decisions. This work plans to use the techniques of explainable machine learning to develop the appropriate model for empirical data with high prediction and good interpretability. In this study, we use the data provided by Taipei City Center for Prevention of Domestic Violence and Sexual Assault (臺北市家庭暴力暨性侵害防治中心) to develop the risk prediction model to predict the recurrence probability of violence accident for the same case before this case is resolved. This prediction model can also provide individual feature explanation and the counterfactual explanation to help social workers conduct an intervention for violence prevention.
Jo-Yu, Huang y 黃若瑜. "Cognitive Style and Prior Knowledge Effect Study on Scientific Prediction, Simulation, Observation and Explanation Inquiry-Baed Learning Activity". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/86542084063693838782.
Texto completo國立中央大學
學習與教學研究所
104
Inquiry-based learning approach plays an important role in science education, and technology has demonstrated its potential on enhancing inquiry-based learning performance. In this study,the author integrates simulation technology and POE (Prediction-Observation-Explanation)learning approach into a revised model named PSOE (Prediction-Simulation-Observation-Explanation). Sixty junior high school students were involved in this study lasting for seven weeks to evaluate the PSOE model. To provide the students a physical simulation environment,a platform, Algodoo, developed by Algoryx company was adopted in which the students can explore and observer the simulation results by manipulating the variables. Moreover, to explore the human factors affected learning performance, different cognitive style (field-dependent and field-independent students) and prior knowledge students (high prior knowledge and low prior knowledge students) were employed. The results reveal that most of the students were weak in the Explanation stage. The results also indicated that the field-independent students had higher score compared to the field-dependent student, and high-prior knowledge students had higher scores than the low-prior knowledge students, but low-prior knowledge students had much improvement than the high-prior knowledge students. Meanwhile, the students’ science attitudes were improved after adopting PSOE inquiry-based learning activity.
Hochstein, Eric. "Intentionality as Methodology". Thesis, 2011. http://hdl.handle.net/10012/6530.
Texto completoKU, PING y 谷冰. "The Comparison of Design Experimental Course and Micro Teaching of Prediction - Observation - More Explanation Teaching Strategy by Different Background Graduate Students". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3z467m.
Texto completo國立臺北教育大學
自然科學教育學系
106
This study with adopted mixed methods of quality and quantity research explores the effect of Prediction - Observation - more Explanation (PO+E) method learned by graduate students of different backgrounds on the ability of experiment design, micro teaching, scientific explanation, and scientific inquiry. 14 graduate students (including 7 in-service teachers and 7 non-service teachers) taking the course of Advanced Entomology in the Department of Science Education were selected. During the course, graduate students first study the general concept of entomology and PO+E and observe teaching demonstrations, and then progress into the first stage of imitation experiment and micro teaching and the second stage of self-designed experiments and micro teaching. The researcher performs the analysis with the collection of the data of PO+E study sheets and written interviews, combined with designed experiment evaluation form and scale, Micro Teaching Assessment of PO+E (MTAP), Assessment of Scientific Interpretation ability (ASI), and Science Inquiry Competence Test (SICT). The research result indicated that after completing this course, graduate students can utilize PO+E teaching strategy with great obvious progress on the ability of designed experiments, micro-teaching, scientific explanation, and scientific inquiry.The comparison between in-service teacher group and non-service teacher group is as follows: First in the designed experiment with PO+E explanations in-service teachers scored higher but the non-service teachers made substantial progress; secondly, in the field of micro-teaching, the in-service teachers are superior to the non-service teachers in terms of scoring and progress; third, the ability of scientific interpretation can be gradually growing, the in-service teachers scored higher, but the non-service teachers has made great progress; fourth, in the scientific inquiry ability aspect, the in-service teachers’ score is high, but the non-service teachers have a large progress. Moreover there is no statistically significant difference in the scientific inquiry ability between in-service teachers and non-in-service teachers before or after class. In a word, graduate students with different backgrounds learning the PO+E teaching method can improve their ability of experiment design, micro-teaching, scientific explanation ability, and scientific inquiry ability. The results of this study can be used for reference in future research.
HUANG, YI-SHENG y 黃奕升. "Elementary Pre-Service Teachers Apply a Prediction-Observation-More Explanation to Design Science Lesson Plans and Teach in the Elementary School". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wpbnc9.
Texto completo國立臺北教育大學
自然科學教育學系
107
This study supported 24 elementary pre-service teachers to design science lesson plans using a Prediction-Observation-more Explanation (PO+E Teaching method), and the "Natural science teaching materials and methods" course in the Department of Science Education provided the training. A mixed method research design was used comprising Quantitative and qualitative research. First, elementary pre-service teachers were instructed in using the PO+E Teaching method and watched case films to see how it could be used (3 weeks). Second, elementary pre-service teachers were divided into eight groups to discuss and plan how to use the PO+E Teaching method to design science lessons (4 weeks). Then, elementary pre-service teachers implemented micro-teaching using the PO+E Teaching method in the class, and the professor and peers provided comments and feedback (4 weeks). Finally, elementary pre-service teachers taught their science lessons in elementary school classrooms, and counselors evaluated their teaching. It took pretest and posttest on elementary pre-service teachers with Science Inquiry Competence Test (SICT) at the course start and the end. SPSS was used to analyses the SICT quantitative data, and the collected science lesson plans, learning sheets, and Micro Teaching Assessment of PO+E (MTAP) were analyzed with Hermeneutic method and Kappa. The results were as follows: 1.All elementary pre-service teachers were able to use the PO+E Teaching method to design science lessons, they achieved good degree. 2.The pre-test and post-test SICT results showed significant difference (p < .05), indicating that elementary pre-service teachers had improved their science inquiry competence skills after completing the course. 3.Qualitative data showed that elementary pre-service teachers familiar with science teaching with the PO+E Teaching method. 4.The PO+E Teaching method was effective in supporting elementary pre-service teachers to design science lesson plans.
"Predicting and Interpreting Students Performance using Supervised Learning and Shapley Additive Explanations". Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53452.
Texto completoDissertation/Thesis
Masters Thesis Computer Science 2019