Academic literature on the topic '3P model of learning'

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Journal articles on the topic "3P model of learning"

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De la Fuente, Jesús, Lucía Zapata Sevillano, Paul Sander, and María Cardelle-Elawar. "THE 3P AND DEDEPRO MODELS AS RESEARCH HEURISTIC." International Journal of Developmental and Educational Psychology. Revista INFAD de Psicología. 4, no. 1 (November 29, 2016): 155. http://dx.doi.org/10.17060/ijodaep.2014.n1.v4.599.

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Abstract.There are different heuristics that study the teaching-learning process; in this investigation we will explore the confluence of Biggs’ 3P model and the DEDEPRO model. These two complementary models offer us a framework for the analysis of teaching-learning situations with greater specificity and a better understanding of the structure of the research studies and the variables they study. In this manner, by incorporating both the general processes of teaching and learning, as well as the specific variables that are related to them, more or less analytical studies can be developed. Biggs adopted the 3P model to represent the student’s perspective in the teaching-learning process. The ways students learn are explained through the interaction of three moments in time that become the components for which the model is named: 1) Presage, where we find student characteristics and characteristics of the learning context; 2) Process, referring to the way that learning tasks are undertaken; and 3) Product, which includes learning outcomes. All the components that make up this model (Presage-Process-Product) tend toward equilibrium, and a change in any of them affects the system as a whole. In complementary fashion, the DEDEPRO model has established the need to further specify, within the Biggs model, the moments of Design (planning), Development (execution) and Product (satisfaction and performance), in terms of both teaching and learning. The DEDEPRO model assumes personal self-regulation, and is interactive with regulation in teaching, thereby giving rise to different levels of performance and personal satisfaction. In an overall sense, both models give us the opportunity to organize our variables over the teaching-learning process. We would emphasize the joint structure of the two models in relation to the variables in this study: Personal self-regulation, Stressful context (Presage); Learning approaches, Coping strategies, Self-regulated learning and Regulatory teaching (Process-Development); and Performance and Satisfaction with the learning process (Product).Palabras Clave: Biggs’ 3P Model, DEDEPRO, Teaching-Learning process
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Kanashiro, Patricia, Edson Sadao Iizuka, Caio Sousa, and Suzi Elen FeRReira Dias. "Sustainability in management education: a Biggs’ 3P model application." International Journal of Sustainability in Higher Education 21, no. 4 (April 13, 2020): 671–84. http://dx.doi.org/10.1108/ijshe-05-2019-0176.

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Purpose The purpose of this paper is to assess the main factors that contribute to teaching and learning sustainability in management education (SiME), which is defined as a body of knowledge that meets the needs of both current and future generations of students. Design/methodology/approach The authors adopt the Biggs’ Presage–Process–Product (3P) learning model to guide and categorize the findings from the literature review on SiME. This study provides an overview of journal articles published between 2002 and 2017. Findings In the context of SiME, Biggs’ 3P model shows how teaching context and students’ background (presage factors) influence students’ approaches to learning, which can range from surface to deep learning (process) and result in various learning outcomes (products). Research limitations/implications The literature review may have excluded important and relevant work from the sample. Practical implications This review highlights that personal and institutional commitments are necessary to promote effective learning of sustainability. Social implications Effective learning outcomes (deep learning) in sustainability should encourage students to reflect on their personal values and behaviors and to acquire analytical skills aimed at promoting conservation and remediation of social, environmental and economic problems. Originality/value This paper provides an application of Biggs’ 3P learning model in the context of sustainability, which highlights the conditions for deep learning as critical given the complexity and urgency of addressing sustainability crises.
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Pane, Katia, Mario Zanfardino, Anna Maria Grimaldi, Gustavo Baldassarre, Marco Salvatore, Mariarosaria Incoronato, and Monica Franzese. "Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB." Biomedicines 10, no. 6 (June 2, 2022): 1306. http://dx.doi.org/10.3390/biomedicines10061306.

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Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.
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Zhang, Li-Fang. "University Students' Learning Approaches in Three Cultures: An Investigation of Biggs's 3P Model." Journal of Psychology 134, no. 1 (January 2000): 37–55. http://dx.doi.org/10.1080/00223980009600847.

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Jae Hoon, Han, Kwon Suk Jin, and Park Jong Sun. ""(Re)Binding the Factors Affecting Student Learning Outcomes in a Cyber University Using the 3P Model: Learning Analytics Approaches." Korean Association for Educational Information and Media 21, no. 2 (June 30, 2015): 309–32. http://dx.doi.org/10.15833/kafeiam.21.2.309.

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Choi, Mi-Ran, Hyo-Jeong So, and Young-Ju Joo. "Analysis of learning approach and outcome variables among credit bank-based distance learners: Focusing on the 3P learning system model." Korean Association for Educational Information and Media 23, no. 3 (September 30, 2017): 605–30. http://dx.doi.org/10.15833/kafeiam.23.3.605.

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Rao, Zhenhui, and Lin Huang. "Exploring the effects of school context on Chinese students’ use of language learning strategies in English learning." Applied Linguistics Review 10, no. 2 (May 26, 2019): 117–36. http://dx.doi.org/10.1515/applirev-2017-0022.

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AbstractThe research reported here investigated the effects of contextual factors on Chinese students’ use of language learning strategies in English learning. The study differed from most of the previous studies on learning strategies in that the data were examined within Biggs (1993. What do inventories of students’ learning processes really measure? A theoretical review and clarification. British Journal of Educational Psychology 63. 3–19.) 3P (Presage, Process and Product) model of learning. Using a Strategy Inventory for Language Learning (Oxford, R. L. 1990. Language learning strategies: What every teacher should know. New York: Newbury House Publishers.) and interview, the researchers discovered the disparities in the use of learning strategies in English learning between the students from urban schools and those from rural schools. The former made a more frequent use of the strategies leading to the improvement of communicative competence and active involvement in classroom activities, whereas the latter showed a strong preference for the strategies relevant to the analysis of grammatical rules and linguistic details. An in-depth analysis of the data revealed that the differences in strategy use between the two groups might stem from some social and economic factors and could be linked to three elements in Biggs’ 3P model: value and expectation, teacher competence and teaching facilities. The article concludes by discussing some implications for English as a foreign language (EFL) teaching in Chinese schools and other similar education settings worldwide.
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Caro Torres, María Catalina, and Diana Angélica Parra Pérez. "Effects of a Blended-Flipped English program on the learning of Academic and Administrative Staff in a Higher Education Institution." International Journal of Applied Linguistics and English Literature 8, no. 3 (May 31, 2019): 118. http://dx.doi.org/10.7575/aiac.ijalel.v.8n.3p.118.

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This article presents the results of an exploratory study about the contributions of a blended English as a Foreign Language (EFL) learning program, with a flipped classroom instructional model, to the development of the listening and reading skills of academic and administrative staff of a higher education institution. The research followed a mixed method approach framed on variables such as the development of oral (listening) and written (reading) comprehension activities. The results present some contributions of the blended-flipped instructional model to the EFL teaching and learning processes, as well as to development of professors’ and administrative staff’s communicative competences in English. This study highlights how the combination of blended learning with a flipped classroom approach to proficiency program design opens new possibilities in the language learning instructional design field, and demonstrates the blended-flipped model’s positive effects on language learning. It is also novel in its proof of the effectiveness of creating a program based on the professional development needs of the University’s community of professors and administrative staff from various disciplines.
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Kember, David, Beverley J. Webster, and Wincy S. C. Chan. "Refocusing the 3P model to incorporate a learning and teaching environment and graduate attributes." Educational Psychology 40, no. 5 (March 4, 2020): 592–607. http://dx.doi.org/10.1080/01443410.2020.1732304.

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López-García, Alejandro, Pedro Miralles-Martínez, and Javier Maquilón. "Design, Application and Effectiveness of an Innovative Augmented Reality Teaching Proposal through 3P Model." Applied Sciences 9, no. 24 (December 11, 2019): 5426. http://dx.doi.org/10.3390/app9245426.

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Augmented reality (AR) has evolved hand in hand with advances in technology, and today is considered as an emerging technique in its own right. The aim of our study was to analyze students’ perceptions of how useful AR is in the school environment. A non-experimental quantitative design was used in the form of a questionnaire in which 106 primary sixth-grade students from six schools in the Region of Murcia (Spain) participated. During the study, a teaching proposal using AR related to the content of some curricular areas was put forward in the framework of the 3P learning model. The participants’ perceptions of this technique were analyzed according to each variable, both overall and by gender, via a questionnaire of our own making, which had previously been validated by AR experts, analyzing its psychometric qualities. The initial results indicate that this technique is, according to the students, useful for teaching the curriculum. The conclusion is that AR can increase students’ motivation and enthusiasm while enhancing teaching and learning at the same time.
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Dissertations / Theses on the topic "3P model of learning"

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Jones, Catherine Toni, and n/a. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning." Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030304.092316.

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The aim of this research programme was to examine the 3P model of learning (Biggs, 1987a, 1999). The first stage necessarily involved an examination of the Study Process Questionnaire (SPQ) (Biggs, 1987a), an instrument developed to measure the process component of the model. The structure of the SPQ was examined utilising exploratory and confirmatory factor analysis of undergraduate responses (n= 260). The results indicated the higher-order factor structure of deep-achieving and surface-achieving-motive provided the most reliability and a better model fit than either the subscales or scales of the SPQ. The construct validity of the two constructs deep and surface was assessed next using a multitrait-multimethod matrix (MTMM) constructed from the three measures of the self-report questionnaire, interview ratings and written assessments from first-year students (n = 50). The results indicated good convergent validity between the deep scale of the SPQ and the interview ratings on the deep scale, between the deep scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The results indicated good convergent validity between the surface scale on the SPQ and the interview ratings on the surface scale, but not between the surface scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The discriminant validity between deep and surface was good for the SPQ, but not for either the interview or the written assessment. The findings indicate the deep and surface scales of the SPQ adequately measure the underlying deep and surface constructs. The retest reliability of the SPQ was then examined utilising Spearman’s Rho to assess the rank-order correlations with a sample of third-year students (n=87). Over a period of three months there were significant correlations for the surface motive, surface strategy, deep strategy, achieving motive and achieving strategy subscales of the SPQ, suggesting good reliability for these subscales. The results at the scale level of the SPQ result in similar conclusions. There was a moderate significant correlation for the surface, deep and achieving scales of the SPQ, suggesting the scales have good reliability over a period of three months. There was also a moderate significant correlation for the surface-achieving-motive and deep-achieving scales over a period of three months. The stability of SPQ scores was also assessed utilising a series of one-way repeated measures MANOVA’s with a sample of third-year undergraduates (n = 64). The results suggest some change occurs in self-reported use of approaches to learning between the first and third-years of an undergraduate degree programme. The role of the teaching-learning environment was next examined. Utilising a within-subjects design, undergraduate students (n=48) concurrently enrolled in traditional (viz. lecture and tutorial) and non-traditional (viz. workshops and group projects) subjects completed the SPQ to describe their approaches to learning in each subject. A series of 2x2 repeated measures MANOVA’s were undertaken. The results indicated students were likely to change their approach to learning based on their perceptions of the learning environment (traditional or non-traditional subject). However, those students identified as predominantly surface learners significantly increased their deep scale scores in the non-traditional subject when compared to deep learners. The next study examined a range of personality (locus of control, sensing function, thinking function, intelligence) and demographic variables (age, gender, year of study) to assess which were good predictors of deep and surface approaches to learning. A series of regression analyses identified age, sensing function and locus of control as significant predictors of the surface, surface-achieving-motive, and deep approaches to learning. Locus of control was found to be a significant predictor of the deep-achieving approach to learning. The final study examined the 3P model of learning. Based on the results of earlier studies in the research programme the situational component of the presage factors was not included. The model was examined using structural equation modelling (n= 394). Two initial models were tested using both the three (deep, surface, achieving) and two (surface-achieving-motive and deep-achieving) process factor models. The three process factor model provided the better model fit. The results suggest deep and surface approaches to learning do not mediate between personal characteristics and learning outcomes (i.e. GPA). The results of this series of studies suggest the need for further research into the SPQ and the 3P model of learning. The implications of the research programme are also discussed.
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Jones, Catherine Toni. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366357.

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The aim of this research programme was to examine the 3P model of learning (Biggs, 1987a, 1999). The first stage necessarily involved an examination of the Study Process Questionnaire (SPQ) (Biggs, 1987a), an instrument developed to measure the process component of the model. The structure of the SPQ was examined utilising exploratory and confirmatory factor analysis of undergraduate responses (n= 260). The results indicated the higher-order factor structure of deep-achieving and surface-achieving-motive provided the most reliability and a better model fit than either the subscales or scales of the SPQ. The construct validity of the two constructs deep and surface was assessed next using a multitrait-multimethod matrix (MTMM) constructed from the three measures of the self-report questionnaire, interview ratings and written assessments from first-year students (n = 50). The results indicated good convergent validity between the deep scale of the SPQ and the interview ratings on the deep scale, between the deep scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The results indicated good convergent validity between the surface scale on the SPQ and the interview ratings on the surface scale, but not between the surface scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The discriminant validity between deep and surface was good for the SPQ, but not for either the interview or the written assessment. The findings indicate the deep and surface scales of the SPQ adequately measure the underlying deep and surface constructs. The retest reliability of the SPQ was then examined utilising Spearman’s Rho to assess the rank-order correlations with a sample of third-year students (n=87). Over a period of three months there were significant correlations for the surface motive, surface strategy, deep strategy, achieving motive and achieving strategy subscales of the SPQ, suggesting good reliability for these subscales. The results at the scale level of the SPQ result in similar conclusions. There was a moderate significant correlation for the surface, deep and achieving scales of the SPQ, suggesting the scales have good reliability over a period of three months. There was also a moderate significant correlation for the surface-achieving-motive and deep-achieving scales over a period of three months. The stability of SPQ scores was also assessed utilising a series of one-way repeated measures MANOVA’s with a sample of third-year undergraduates (n = 64). The results suggest some change occurs in self-reported use of approaches to learning between the first and third-years of an undergraduate degree programme. The role of the teaching-learning environment was next examined. Utilising a within-subjects design, undergraduate students (n=48) concurrently enrolled in traditional (viz. lecture and tutorial) and non-traditional (viz. workshops and group projects) subjects completed the SPQ to describe their approaches to learning in each subject. A series of 2x2 repeated measures MANOVA’s were undertaken. The results indicated students were likely to change their approach to learning based on their perceptions of the learning environment (traditional or non-traditional subject). However, those students identified as predominantly surface learners significantly increased their deep scale scores in the non-traditional subject when compared to deep learners. The next study examined a range of personality (locus of control, sensing function, thinking function, intelligence) and demographic variables (age, gender, year of study) to assess which were good predictors of deep and surface approaches to learning. A series of regression analyses identified age, sensing function and locus of control as significant predictors of the surface, surface-achieving-motive, and deep approaches to learning. Locus of control was found to be a significant predictor of the deep-achieving approach to learning. The final study examined the 3P model of learning. Based on the results of earlier studies in the research programme the situational component of the presage factors was not included. The model was examined using structural equation modelling (n= 394). Two initial models were tested using both the three (deep, surface, achieving) and two (surface-achieving-motive and deep-achieving) process factor models. The three process factor model provided the better model fit. The results suggest deep and surface approaches to learning do not mediate between personal characteristics and learning outcomes (i.e. GPA). The results of this series of studies suggest the need for further research into the SPQ and the 3P model of learning. The implications of the research programme are also discussed.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
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Tai, Chunming. "Undergraduate business and management students' experiences of being involved in assessment." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/9456.

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This study aimed to explore university undergraduates’ experiences of student involvement in assessment (SIA). Based on Biggs’ 3P model of student learning, this study focused on students’ experiences prior to SIA, during SIA and after SIA in three Business and Management modules. Applying this framework, different practices of involving students in assessment (peer assessment, self assessment or self designed assessment) were studied from the perspectives of the students concerned. Unlike other studies that normally test to what extent the designed outcomes of SIA have been met, the goal of this research was to reveal the inside picture of how students were coping with those SIA tasks and their learning. This picture was outlined from students’ perceptions of SIA, the main factors that might influence students’ engagement with SIA, and students’ reflections on SIA practice in the particular module. This study adopted mixed research methods with sequential explorative design. It employed the ETLA (Environment of Teaching, Learning and Assessment) questionnaire and follow up semi-structured interviews. There were in total 251 valid questionnaire responses from students and 18 valid student interviews. The data were collected from three undergraduate Business and Management degree modules in which different strategies were used to involve students in assessment. The three innovative modules were all from Scottish universities in which assessment practices were being re-engineered by involving students in assessment. Two of the modules had participated in the REAP (Re-engineering Assessment Practice) project. However, they were different from each other in terms of the way in which they involved students in assessment and the level or extent of student involvement in assessment that was entailed. The report and analysis of the findings has taken three main forms. First, the module context including the teaching, learning and assessment environment and student learning approaches and satisfactions in the particular module were compared and analysed using the questionnaire data. The results showed a strong association between the elements in the teaching and learning environment and student learning approaches. They also indicated that the quality of teaching, feedback and learning support played significant roles in the quality of student learning. Secondly, an analysis of the interview data was undertaken to examine why and how students would learn differently in different module contexts with different SIA practices, and how students were coping with their learning in the SIA tasks concerned. In addressing these questions, students’ previous experiences in SIA, and knowledge about SIA, peers’ influence, teachers’ support and training for SIA, interaction between and among students and teachers, the clarity of the module objectives and requirements and learning resources were found to be the major factors that might influence students’ engagement in the SIA. Additionally, the salient learning benefits and challenges of SIA as perceived by students were explored. Thirdly, based on the preceding findings, the analysis of each module aimed to further consider in what way the three modules differed from each other with respect to SIA practices, and how students responded in the three different module contexts in terms of their engagement with SIA. These three forms of analysis made it possible to gain a rich understanding of students’ experiences of SIA that could also feed into a consideration of what kind of support the students might need in order to better engage them into the SIA and better prepare them for life-long learning.
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Draper, Fiona J. "Development of a Student-Centred Evaluation Framework for Environmental Vocational Education and Training Courses. Development and validation of a Student-Centred Evaluation Framework for Environmental Vocational Education and Training Courses derived from Biggs' 3P Model and Kirkpatrick's Four Levels Evaluation Model." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5496.

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Individuals and organisations need to do much more if sustainable development is to be achieved. Appropriate environmental vocational education and training (EVET) is essential for current decision makers. Crucial decisions need to be made before the present generation of school and college students achieve significant positions of authority. An increasing range of EVET courses and course providers are available within the UK. However, availability is not synonymous with suitability for either the attendee and/or his/her (future) employer. Previous research indicates that, as a component of lifelong learning, EVET courses should and the methods used to evaluate them should be student-centred. This thesis describes the development and validation of a new studentcentred evaluation framework. Preliminary literature reviews identified six fundamental issues which needed to be addressed. Existing academically productive evaluation models were examined and critically appraised in the context of these problems. The output from this process was used to develop a bespoke research methodology. Empirical research on four commercial EVET programmes revealed distinct personal, teaching and work-based presage factors which influenced course attendance, individual learning and subsequent organisational learning. Modified versions of Biggs' 3P model and Kirkpatrick's Four level Evaluation Model were shown to provide an effective student-centred evaluation framework for EVET courses. Additional critical elements pertaining course utility and the student's long(er) term ii retention of knowledge/skill were derived from previous research by Alliger et al (1997). Work-based presage factors and the student¿s return on expectation were added as a direct consequence of this research. The resultant new framework, the Presage-Product Evaluation Framework, was positively received during an independent validation. This confirmed inter alia that the framework should also be capable of adaption for use with other VET courses. Recommendations for additional research focus on the need to demonstrate this through further empirical studies.
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Draper, Fiona Jane. "Development of a student-centred evaluation framework for environmental vocational education and training courses : development and validation of a student-centred evaluation framework for environmental vocational education and training courses derived from Biggs' 3P Model and Kirkpatrick's Four Levels Evaluation Model." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5496.

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Individuals and organisations need to do much more if sustainable development is to be achieved. Appropriate environmental vocational education and training (EVET) is essential for current decision makers. Crucial decisions need to be made before the present generation of school and college students achieve significant positions of authority. An increasing range of EVET courses and course providers are available within the UK. However, availability is not synonymous with suitability for either the attendee and/or his/her (future) employer. Previous research indicates that, as a component of lifelong learning, EVET courses should and the methods used to evaluate them should be student-centred. This thesis describes the development and validation of a new studentcentred evaluation framework. Preliminary literature reviews identified six fundamental issues which needed to be addressed. Existing academically productive evaluation models were examined and critically appraised in the context of these problems. The output from this process was used to develop a bespoke research methodology. Empirical research on four commercial EVET programmes revealed distinct personal, teaching and work-based presage factors which influenced course attendance, individual learning and subsequent organisational learning. Modified versions of Biggs¿ 3P model and Kirkpatrick¿s Four level Evaluation Model were shown to provide an effective student-centred evaluation framework for EVET courses. Additional critical elements pertaining course utility and the student¿s long(er) term ii retention of knowledge/skill were derived from previous research by Alliger et al (1997). Work-based presage factors and the student¿s return on expectation were added as a direct consequence of this research. The resultant new framework, the Presage-Product Evaluation Framework, was positively received during an independent validation. This confirmed inter alia that the framework should also be capable of adaption for use with other VET courses. Recommendations for additional research focus on the need to demonstrate this through further empirical studies.
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Huang, Mei-hui. "Factors influencing self-directed learning readiness amongst Taiwanese nursing students." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/20709/1/Mei-hui_Huang_Thesis.pdf.

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Rapid scientific and technological advances in health care mean that nurses need to keep learning and engage in professional education so that they can continue to provide safe and quality care. Education programs which prepare nurses for practice as a registered nurse have a vital role to play in ensuring that graduates are self-directed in responding to the need for ongoing learning throughout their professional career. In many countries, improving students’ readiness for self-directed learning has thus gained increasing recognition as being an important goal of nursing education programs. This level of interest in developing self-directedness in learning is evident in many policy documents and research in Taiwan. The aim of this study was to investigate factors influencing self-directed learning readiness amongst Taiwanese nursing students. A conceptual framework adopted from Biggs’s ‘3P model of teaching and learning’ was constructed to guide this study’s investigation. This study employed a two-staged mixed-method design to obtain a better understanding of Taiwanese students’ experience of SDL in undergraduate nursing programs. Stage one of the present study was a qualitative approach using semi-structured interview to explore students’ experiences with learning activities which they perceived to be self-directed in their undergraduate programs. Eight students were interviewed. Findings from this stage reveal that participants perceived a shift in teaching and learning styles between their previous nursing programs and the university. The more frequent use of student-directed learning activities, in which students were encouraged to be active and to take responsibility for their learning tasks, was one of the changes in teaching and learning approaches perceived by participants. Participants further suggested a number of factors that influenced the outcomes of these learning activities, including teacher-student interaction, facilitation process and learning resources. Stage two of this study used a quantitative approach consisting of two phases: instrument pilot testing and a cross-sectional survey. In the first phase, the instruments were translated into Chinese through a rigorous translation process and tested with a convenience sample of nursing students in Taiwan. Results indicated the translated instruments were reliable and stable. The second phase, a cross-sectional survey, was conducted to examine the conceptual framework of this study. A total of 369 undergraduate nursing students completed the questionnaire. Results of data analysis provides support for the conceptual framework proposed for this study, suggesting that students’ achievement goals and their perceptions of the learning environment significantly influence their adoption of learning approaches and the development of SDL readiness. Based on the results, this study provides practical implications that nurse educators may adopt to enhance students’ SDL readiness. This study also provides theoretical implications and recommendations for future research. It is envisaged that these recommendations may help future researchers focus their research design and further understandings of how to help students develop their ability to become self-directed learners.
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Huang, Mei-hui. "Factors influencing self-directed learning readiness amongst Taiwanese nursing students." Queensland University of Technology, 2008. http://eprints.qut.edu.au/20709/.

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Rapid scientific and technological advances in health care mean that nurses need to keep learning and engage in professional education so that they can continue to provide safe and quality care. Education programs which prepare nurses for practice as a registered nurse have a vital role to play in ensuring that graduates are self-directed in responding to the need for ongoing learning throughout their professional career. In many countries, improving students’ readiness for self-directed learning has thus gained increasing recognition as being an important goal of nursing education programs. This level of interest in developing self-directedness in learning is evident in many policy documents and research in Taiwan. The aim of this study was to investigate factors influencing self-directed learning readiness amongst Taiwanese nursing students. A conceptual framework adopted from Biggs’s ‘3P model of teaching and learning’ was constructed to guide this study’s investigation. This study employed a two-staged mixed-method design to obtain a better understanding of Taiwanese students’ experience of SDL in undergraduate nursing programs. Stage one of the present study was a qualitative approach using semi-structured interview to explore students’ experiences with learning activities which they perceived to be self-directed in their undergraduate programs. Eight students were interviewed. Findings from this stage reveal that participants perceived a shift in teaching and learning styles between their previous nursing programs and the university. The more frequent use of student-directed learning activities, in which students were encouraged to be active and to take responsibility for their learning tasks, was one of the changes in teaching and learning approaches perceived by participants. Participants further suggested a number of factors that influenced the outcomes of these learning activities, including teacher-student interaction, facilitation process and learning resources. Stage two of this study used a quantitative approach consisting of two phases: instrument pilot testing and a cross-sectional survey. In the first phase, the instruments were translated into Chinese through a rigorous translation process and tested with a convenience sample of nursing students in Taiwan. Results indicated the translated instruments were reliable and stable. The second phase, a cross-sectional survey, was conducted to examine the conceptual framework of this study. A total of 369 undergraduate nursing students completed the questionnaire. Results of data analysis provides support for the conceptual framework proposed for this study, suggesting that students’ achievement goals and their perceptions of the learning environment significantly influence their adoption of learning approaches and the development of SDL readiness. Based on the results, this study provides practical implications that nurse educators may adopt to enhance students’ SDL readiness. This study also provides theoretical implications and recommendations for future research. It is envisaged that these recommendations may help future researchers focus their research design and further understandings of how to help students develop their ability to become self-directed learners.
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Mahadevan, Shankar. "A Learning Object Model For Electronic Learning." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34060.

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Digital libraries are fast expanding into the role of independent educational entities that aspire not only to complementing traditional classroom teaching, but also allow open electronic learning for distance and continued education. These multifaceted roles can be realized only if the course content and the related content management system are versatile enough to be captured into any individual's learning needs. Many studies have defined a concept of "learning object" to address the issues and needs. But in attempting to solve the problem, the definitions have emphasized some aspects of the digital library while leaving the other issues to be solved later. Thus, the whole system dynamics is either weak or too cumbersome to navigate. As a part of this masters work, firstly the current model of pedagogical endowment was investigated. In order to accommodate the digital nature of education, a new modern profile of learning is proposed that allows modular yet efficient transfer of knowledge from the teacher to the pupil. The thesis then proposes a comprehensive learning object (LO) model, along with the associated system model, that will allow complete and flexible integration of content into the modern digital library profile. The process will be user-centric (both for knowledge developers and learners) as well as metadata-centric. It is scalable and interoperable with legacy and existing content databases and display systems. This thesis covers how the LO model is integrated into the core of the library's content development, discovery, and delivery process. The results of the experiment in terms of ease-of-use, flow-control, and feasibility of the model are documented. A beta-version of these concepts has been successfully tested with volunteers and implemented as a part of the Digital Library Network for Engineering and Technology (DLNET) project.
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GHADIRZADEH, ALI. "LEARNING A VISUALFORWARD MODEL." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142034.

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Abstract Internal forward models are aimed to provide the system with the prediction of changes in sensory observations as the consequent of its own actions. For the special case where the sensed information is in the form of the camera images, the model is called visual forward model. Images are one of the richest resources of data and the ability to predict the sensory camera images, enables the robots to do more autonomous and intelligent tasks. Most of actions performed by robots lead to outcomes which are appearing in the vision system. Therefor the capability to predict these outcomes in the form of images helps the robot to execute better long- term plans. That is why the visual forward models are of particular importance. The main challenges regarding the construction of the visual forward models are the high amount of image data to be predicted and the degrees of freedom of the robot's action which causes the complexities to grow rapidly. In this work, we have investigated dierent methods to construct the visual forward models for a robotic camera head setup. The forward model explores the contin- gencies between the movements in the robot's neck and eye joints and the resulting changes in the camera images. Four dierent methods to construct the visual for- ward models are introduced and implemented. Learning of the forward models in these methods is based on linear interpolation, radial basis function networks or Gaussian processes given the correspondences between the successive frames ex- tracted by the use of SURF descriptors or constructions of so-called cumulator units. To examine the performance of the proposed methods, two dierent types of experiments are designed with the dierence that in the rst experiment, depth information is not relevant while in the second one it is. Our experimental results show the success of the introduced methods in the construction of the visual forward models also provide the weak and strong aspects of each method.
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Dorfler, Viktor. "Model of learning ability." Thesis, University of Strathclyde, 2005. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=9341.

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Books on the topic "3P model of learning"

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Bu, Lingguo, and Robert Schoen, eds. Model-Centered Learning. Rotterdam: SensePublishers, 2011. http://dx.doi.org/10.1007/978-94-6091-618-2.

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Shell, Duane F., David W. Brooks, Guy Trainin, Kathleen M. Wilson, Douglas F. Kauffman, and Lynne M. Herr. The Unified Learning Model. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3215-7.

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Olderog, Ernst-Rüdiger, Bernhard Steffen, and Wang Yi, eds. Model Checking, Synthesis, and Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91384-7.

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Alderman, Belle Y. A model for work-based learning. Lanham, Md: Scarecrow Press, 2005.

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Mohamed, Khaled Salah. Machine Learning for Model Order Reduction. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75714-8.

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Sultanik, Aaron. Camera-cut-composition: A learning model. New York: Cornwall Books, 1995.

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Wisconsin Model Early Learning Standards Steering Committee. Wisconsin model early learning standards: With introduction. Madison, WI: Wisconsin Department of Public Instruction, 2013.

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Levy, Margaret. Work based learning: A good practice model. Bristol: Staff College, 1991.

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Carroll, John. Learning to fly radio control model airplanes. [Milwaukee, WI: Kalmbach Pub. Co., 1985.

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Tice, Carol M. Intergenerational linkages: The teaching-learning communities model. Ypsilanti, Mich: Institute for the Study of Children and Families, Eastern Michigan University, 1987.

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Book chapters on the topic "3P model of learning"

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Metzler, Donald. "Model Learning." In A Feature-Centric View of Information Retrieval, 121–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22898-8_6.

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Der, Ralf, and Georg Martius. "Model Learning." In Cognitive Systems Monographs, 183–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20253-7_9.

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Keay, Jeanne K., and Christine M. Lloyd. "The Model." In Linking Children’s Learning With Professional Learning, 87–102. Rotterdam: SensePublishers, 2011. http://dx.doi.org/10.1007/978-94-6091-645-8_7.

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Shell, Duane F., David W. Brooks, Guy Trainin, Kathleen M. Wilson, Douglas F. Kauffman, and Lynne M. Herr. "Learning." In The Unified Learning Model, 7–17. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3215-7_2.

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Horn, Carol V., Catherine A. Little, Kirsten Maloney, and Cheryl McCullough. "Professional Learning." In Young Scholars Model, 97–118. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003239697-6.

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Buckley, Barbara C. "Model-Based Learning." In Encyclopedia of the Sciences of Learning, 2300–2303. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_589.

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Rose, Jonas, and Robert Schmidt. "Discrimination Learning Model." In Encyclopedia of the Sciences of Learning, 1013–15. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_343.

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Spector, Michael J. "Model-Facilitated Learning." In Encyclopedia of the Sciences of Learning, 2316–17. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_378.

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Kumar, R. Santhosh, and M. Kalaiselvi Geetha. "Deep Learning Model." In Data Science, 305–22. Boca Raton : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9780429263798-14.

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Jones, Kevin Anthony, and Ravi S. Sharma. "The Learning Model." In Higher Education 4.0, 89–101. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6683-1_4.

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Conference papers on the topic "3P model of learning"

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Song, Jun. "Elements in MOOC-based College English Learning Environment--Based on Biggsrs 3P Model." In 4th International Symposium on Social Science (ISSS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/isss-18.2018.74.

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Jia, Yinghua, and Xiao Wei. "3P Strategy Based on Vocabulary Memorization Model." In 2015 11th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2015. http://dx.doi.org/10.1109/skg.2015.33.

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Binkowski, Tomasz. "Current trajectory model of unbalanced load used for 3P-3W voltage source inverter control." In 2018 XIV-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH). IEEE, 2018. http://dx.doi.org/10.1109/memstech.2018.8365701.

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Jinju Duan and Yun Qi. "An Online curriculum design framework based on 3P model from the perspective of knowledge transfer." In 2014 9th International Conference on Computer Science & Education (ICCSE). IEEE, 2014. http://dx.doi.org/10.1109/iccse.2014.6926554.

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Dolan, Dale, and P. W. Lehn. "Simulation Model of Wind Turbine 3p Torque Oscillations due to Wind Shear and Tower Shadow." In 2006 IEEE PES Power Systems Conference and Exposition. IEEE, 2006. http://dx.doi.org/10.1109/psce.2006.296240.

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Gross, Andrew M., Ryan K. Orosco, John P. Shen, Ann Marie Egloff, Hannah Carter, Matan Hoffree, Michel Choueiri, et al. "Abstract 3821: A prognostic model of head and neck cancer ties TP53 mutation to 3p loss." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-3821.

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Allison, Jordan. "The Importance of Context: Assessing the Challenges of K-12 Computing Education Through the Lens of Biggs 3P Model." In Koli Calling '21: 21st Koli Calling International Conference on Computing Education Research. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3488042.3488043.

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Thys, Maxime, Valentin Chabaud, Thomas Sauder, Lene Eliassen, Lars O. Sæther, and Øyvind B. Magnussen. "Real-Time Hybrid Model Testing of a Semi-Submersible 10MW Floating Wind Turbine and Advances in the Test Method." In ASME 2018 1st International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/iowtc2018-1081.

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This article presents the Real-Time Hybrid Model (ReaTHM®) tests that were performed on a 10-MW semi-submersible floating wind turbine in the Ocean Basin at SINTEF Ocean in March 2018. The ReaTHM test method was used for the model tests to circumvent the limitations encountered when performing model tests with wind and waves. The physical model was subject to physical waves, while the rotor and tower loads were simulated in real-time and applied on the model by use of a cable-driven parallel robot. Recent advances in the ReaTHM test method allowed for extended testing possibilities and load application up to the 3p frequency and the first tower bending frequency.
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Littman, Michael. "Autonomous Model Learning for Reinforcement Learning." In 2008 Fifth International Conference on Quantitative Evaluation of Systems. IEEE, 2008. http://dx.doi.org/10.1109/qest.2008.48.

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Anita and Naresh Chauhan. "Agile learning model: Self centric learning." In 2014 IEEE International Conference on MOOC, Innovation and Technology in Education (MITE). IEEE, 2014. http://dx.doi.org/10.1109/mite.2014.7020307.

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Reports on the topic "3P model of learning"

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Niyogi, Partha, and Robert C. Berwick. Formalizing Triggers: A Learning Model for Finite Spaces. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada276776.

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Bajari, Patrick, Denis Nekipelov, Stephen Ryan, and Miaoyu Yang. Demand Estimation with Machine Learning and Model Combination. Cambridge, MA: National Bureau of Economic Research, February 2015. http://dx.doi.org/10.3386/w20955.

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Haro, Gloria, Gregory Randal, and Guillermo Sapiro. Translated Poisson Mixture Model for Stratification Learning (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada478474.

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Eaton, Jonathan, Marcela Eslava, David Jinkins, C. Krizan, and James Tybout. A Search and Learning Model of Export Dynamics. Cambridge, MA: National Bureau of Economic Research, July 2021. http://dx.doi.org/10.3386/w29100.

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Subramanian, Devika. Hybrid Learning on the NRL Navigation Task; Fielding a New Hybrid Model of Human Learning. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada419435.

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Merl, D. Advances in Bayesian Model Based Clustering Using Particle Learning. Office of Scientific and Technical Information (OSTI), November 2009. http://dx.doi.org/10.2172/1010386.

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Ganglmair, Bernhard, Timothy Simcoe, and Emanuele Tarantino. Learning When to Quit: An Empirical Model of Experimentation. Cambridge, MA: National Bureau of Economic Research, February 2018. http://dx.doi.org/10.3386/w24358.

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Arifovic, Jasmina, James Bullard, and John Duffy. Learning in a Model of Economic Growth and Development. Federal Reserve Bank of St. Louis, 1995. http://dx.doi.org/10.20955/wp.1995.017.

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Oyen, Diane. Gaining Insight from Data by Learning Graphical Model Structure. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1163637.

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Zheng, Jian. Relational Patterns Discovery in Climate with Deep Learning Model. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, January 2021. http://dx.doi.org/10.7546/crabs.2021.01.05.

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