scholarly journals Evaluating Knowledge and Assessment-Centered Reflective-Based Learning Approaches

2018 ◽  
Author(s):  
Jordi Colomer ◽  
Laura Serra-Saurina ◽  
Dolors Cañabate ◽  
Teresa Serra
Keyword(s):  
Self Regulation ◽  
Reflective Learning ◽  
First Year ◽  
Learning Approach ◽  
Learning Approaches ◽  
Self Reflection ◽  
Learning Framework ◽  
Sustainability Learning ◽  
Self Knowledge

This paper addresses the development of knowledge and assessment-centered learning approaches within a reflective learning framework in a first year physics class in a university faculty. The quality of students’ reflections was scored using a Self-reporting Reflective Learning Appraisal Questionnaire at the end of each learning approach. The results showed the differences between the approaches based on reflections on the learning control through self-knowledge, by connecting experience and knowledge, as well as through self-reflection and self-regulation. Assessment-centered activities fundamentally help students identify aspects of their attitudes towards, as well as regulate, their sustainability learning education.

scholarly journals A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 387-399
Author(s):  
Noor Ifada ◽  
◽  
Richi Nayak ◽  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Approach ◽  
Tensor Model ◽  
Learning Approaches ◽  
Data Sparsity ◽  
Tensor Models ◽  
Sparsity Problem ◽  
Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

scholarly journals BoostTree and BoostForest for Ensemble Learning

2021 ◽  
Author(s):  
Changming Zhao ◽  
Dongrui Wu ◽  
Jian Huang ◽  
Ye Yuan ◽  
Hai-Tao Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Training Data ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Learning Approaches ◽  
Tree Model ◽  
Learning Framework ◽  
Node Splitting ◽  
Reliable Performance ◽  
Classification And Regression

Abstract Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It achieves high randomness (diversity) by sampling its parameters randomly from a parameter pool, and selecting a subset of features randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 34 classification and regression datasets. Remarkably, BoostForest has only one hyper-parameter (the number of BoostTrees), which can be easily specified. Our code is publicly available, and the proposed ensemble learning framework can also be used to combine many other base learners.

scholarly journals THE DEVELOPMENT OF LIFE-LONG LEARNING COMPETENCIES: POSITIONING STUDENTS FOR SELF-REGULATION

2015 ◽  
Author(s):  
Richard J. Aleong ◽  
David S. Strong
Keyword(s):  
Engineering Students ◽  
Sense Of Self ◽  
Self Regulation ◽  
Self Awareness ◽  
Self Reflection ◽  
Reflective Process ◽  
Life Long Learning ◽  
Self Knowledge ◽  
Learning Competencies ◽  
Insight Into

Within the engineering attribute of life-long learning is the ability for self-regulation, described as the process in which students plan, monitor, control, and adjust their behaviour to meet specific goals. To be self-regulating requires a degree of self-awareness and self-reflection to build knowledge about the self. This self-knowledge contributes to one’s values, personal identity, and motivational beliefs that may direct academic behaviour. In this paper, we present insight into the implementation of a workshop program designed to engage undergraduate engineering students in a facilitated self-reflective process. The workshop program challenged participants to think about how they see themselves in their engineering education and how they envision the person they wish to become in their future career. The research aims to offer educators with pedagogical insight into students’ sense of self, self-regulating processes, and new ways to promote the skills of life-long learning.

scholarly journals Comparing students’ perceptions of paper-based and electronic portfolios

2009 ◽  
Vol 34 (3) ◽  
Author(s):  
Maarten Van Wesel ◽  
Anouk Prop
Keyword(s):  
Electronic Portfolio ◽  
Electronic Portfolios ◽  
Perceived Support ◽  
First Year ◽  
Ict Skills ◽  
Self Reflection ◽  
Educational Groups ◽  
Pour Cent ◽  
First Year Medical Students

\Electronic portfolios offer many advantages to their paper-based counterparts, including, but not limited to working on ICT skills, adding multimedia and easier sharing of the portfolio. Previous research showed that the quality of a portfolio does not depend on the medium used. In this article the perceived support for self-reflection of an electronic portfolio and a paper-based portfolio in the same ecological setting are compared. We made use of the fact that during this study about half of the first year medical students was using an electronic portfolio (n = 157) and the other half a paper-based portfolio (n = 190). Nine questions were added to the standard end of the block evaluation, which is handed to 25 percent of year one educational groups. Findings suggest that perceptions about the support for self-reflection, and the usefulness of compiling a portfolio, do not differ between students using an electronic portfolio and students using a paper-based portfolio. Résumé : Les portfolios électroniques offrent de nombreux avantages comparativement à leurs homologues de papier, entre autres la possibilité de perfectionner les compétences liées aux TIC, d’ajouter des éléments multimédias et de partager plus facilement le portfolio. Des études précédentes ont montré que la qualité d’un portfolio ne dépend pas du support utilisé. Dans le présent article, nous comparons l’aide à l’autoréflexion perçue pour un portfolio électronique et un portfolio sur support papier dans le même environnement. Dans le cadre de cette étude, nous avons profité du fait qu’environ la moitié des étudiants de première année en médecine utilisait un portfolio électronique (n = 157) et l’autre moitié, un portfolio sur support papier (n = 190). Neuf questions ont été ajoutées à l’évaluation normale remise à 25 pour cent des groupes de première année à la fin du bloc de formation. Les résultats suggèrent que les perceptions des étudiants à l’égard de l’aide à l’autoréflexion et de l’utilité de compiler un portfolio ne diffèrent pas entre les utilisateurs de portfolios électroniques et les utilisateurs de portfolios sur support papier.

scholarly journals RELATIONS BETWEEN THE PERSONAL SELF-REGULATION AND LEARNING APPROACH, COPING STRATEGIES, AND SELF-REGULATION LEARNING, IN UNIVERSITY STUDENTS (PROCESS)

2016 ◽  
Vol 4 (1) ◽  
pp. 175
Author(s):  
Lucía Zapata ◽  
Jesús De la Fuente ◽  
José Manuel Martínez Vicente ◽  
Mª Carmen González Torres ◽  
Raquel Artuch
Keyword(s):  
Coping Strategies ◽  
Linear Regression Analysis ◽  
Self Regulation ◽  
Approaches To Learning ◽  
Future Research ◽  
Learning Approach ◽  
Learning Approaches ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
The Relationship

Abstract.Introduction. Self-regulation is an important variable in education and research, but in educational context self-regulated learning is the construct more studied. For this, there are a scarcity of studies that seek to establish relationships between personal self-regulation and other educational variables. We aim to delimit the relationships between personal self-regulation (Presage variable) and different process variables: approaches to learning, self-regulated learning and coping strategies, establishing the importance of these variables in future research in meta-cognition. Method. A total of 1101 students participated in the study (university and candidate students). The analyses made to meet the proposed objectives and test hypotheses were: Association analysis through Pearson bivariate correlations (Association objectives and hypotheses); linear regression analysis (Regression objectives and hypotheses); Cluster analysis, ANOVAS and MANOVAS, with Scheffé post hoc, and effect size estimates (Inferential objectives and hypotheses). Results. A significant associative relationship appeared between self-regulation and learning approaches and self-regulated learning; and negative correlation with emotion-focused coping strategies. The different levels of personal self-regulation (presage learning variable) determine of the type of learning approach and of coping strategies. Discussion and Conclusions. The importance of personal self-regulation that determines the degree of cognitive self-regulation during the process of university learning; the relationship between personal self-regulation and the type and quantity of coping strategies, and the relationship between self-regulated learning and coping.Palabras Clave: 3P Model, DEDEPRO Model, Personal Self-regulation, Coping strategies, Selfregulated learning.

Self-Reflection and Recognition: The Role of Metacognitive Knowledge in the Attribution of Recollective Experience

1998 ◽  
Vol 2 (2) ◽  
pp. 111-123 ◽  
Author(s):  
Fritz Strack ◽  
Jens Förster
Keyword(s):  
Social Psychology ◽  
Attribution Theory ◽  
Recognition Task ◽  
Metacognitive Knowledge ◽  
Recollective Experience ◽  
Self Reflection ◽  
Self Knowledge

We argue that to understand how a recognition task is solved, it is helpful to study the inferences that are drawn on the basis of psychological self-knowledge. This claim is supported by findings from 3 experiments in which participants' metacognitive knowledge was either measured or manipulated. Specifically, it was found that when the quality of a recollective experience was not associated with one particular cause, knowledge about whether one would have noted or remembered a stimulus is used. In conclusion, we argue that a perspective that is derived from attribution theory in social psychology may be fruitfully applied to phenomena of recognition.

scholarly journals Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education

2016 ◽  
Vol 3 (2) ◽  
pp. 330-372 ◽  
Author(s):  
Geraldine Gray ◽  
Colm McGuinness ◽  
Philip Owende ◽  
Markus Hofmann
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Tertiary Education ◽  
Early Stage ◽  
Self Regulation ◽  
Support Vector ◽  
First Year ◽  
Learning Approaches ◽  
Students At Risk ◽  
Learning Factor

This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year of study. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrolment data maintained by college administration, and an online, self-reporting, learner profiling tool administered during first-year student induction. Factors considered included prior academic performance, personality, motivation, self-regulation, learning approaches, age and gender.  Models were trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort. A comparison of eight classification algorithms found k-NN achieved best model accuracy (72%), but results from other models were similar, including ensembles (71%), support vector machine (70%) and a decision tree (70%). Models of subgroups by age and discipline achieved higher accuracies, but were affected by sample size; n<900 underrepresented patterns in the dataset. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. This study indicated that early modelling of first year students yielded informative, generalisable models that identified students at risk of failing.

scholarly journals An Active Learning Approach with Uncertainty, Representativeness, and Diversity

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Tianxu He ◽  
Shukui Zhang ◽  
Jie Xin ◽  
Pengpeng Zhao ◽  
Jian Wu ◽  
...  
Keyword(s):  
Active Learning ◽  
Clustering Algorithm ◽  
Ad Hoc ◽  
State Of The Art ◽  
The Internet ◽  
Learning Approach ◽  
Learning Approaches ◽  
Learning Framework ◽  
Query Selection ◽  
The Internet Of Things

Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances’ uncertainty and representativeness to constitute the most informative set. Then, use the kernelk-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.

scholarly journals Development of an instructor guide tool: ‘Three Stages of Holistic Debriefing’

2020 ◽  
Vol 28 ◽  
Author(s):  
Fernanda dos Santos Nogueira de Góes ◽  
Deirdre Jackman
Keyword(s):  
Literature Review ◽  
Reflective Learning ◽  
High Fidelity ◽  
Tool Development ◽  
Pedagogical Practice ◽  
High Fidelity Simulation ◽  
Educator Training ◽  
Learning Framework ◽  
Nursing Educator

Objective: to describe the development of an English and Brazilian Portuguese Holistic Debriefing Tool focused on nursing educator to promote a reflective learning. Method: a methodology study, with three phases: integrative literature review; tool development and review of a panel of nursing experts. The literature review tracked a systematic process. For the tool development were used literature review results, Lederman’s Debriefing Process and Zabala’s learning framework as theoretical referential to promote a reflective learning in High-Fidelity Simulation. The panel of nursing experts analysed the quality of the tool. Results: literature review evidenced gaps about educator pedagogical preparation and indicated no holistic debriefing tool exists which captures formative and summative aspects of debriefing guidance to assist the educator to debrief. Debriefing tool was purposed with two pages: first page were recommended how conduct debriefing and second page is a questions guidance. The tool evaluation was undertaken for a total of three modifications for congruence and concept reader clarity. Conclusion: it was proposed a holistic debriefing tool focused on nursing educator. This study provides an overall picture of the process to promote a reflexive learning in High-Fidelity Simulation and to contribute to formal nursing educator training to apply best pedagogical practice.

scholarly journals A framework for investigating blended learning effectiveness

2014 ◽  
Vol 56 (2/3) ◽  
pp. 233-251 ◽  
Author(s):  
Lily Wong ◽  
Arthur Tatnall ◽  
Stephen Burgess
Keyword(s):  
Blended Learning ◽  
Strong Support ◽  
Learning Approach ◽  
Learning Approaches ◽  
Holistic View ◽  
Content Type ◽  
Face To Face ◽  
Student Readiness ◽  
The Impact

Purpose – The move towards “blended learning”, consisting of a combination of online and face-to-face teaching, continues to gain pace in universities around the world. It is important, however, to question the quality of this learning. The OECD has made use of a model of “Readiness, Intensity and Impact” for investigating the adoption and use of eBusiness technologies. The purpose of this paper is to propose a framework, based on this model and adapted for blended learning, to assess the readiness, intensity of adoption and impact on blended learning offerings. The framework is tested via a description of how one university has adopted and used blended learning, and investigates the quality of the learning from this approach. Design/methodology/approach – The framework is tested via a case study involving the assessment of a blended learning approach to the delivery of a first-year undergraduate accounting unit at Victoria University, Australia. Various approaches to delivery are assessed over a two-year period. The results are drawn from a survey specifically designed to identify students’ attitudes towards blended learning. Findings – Despite having three new online options readily available for students to access, there was strong support for face-to-face delivery methods. In relation to the framework, the assessment suggested that certain aspects of the university's blended learning approach could be investigated further (particularly student readiness for different blended learning options and an overall assessment of the impact of a blended approach), to provide a more holistic view of the readiness to adopt and impact of the blended learning offerings. Originality/value – The value of this contribution lies in the development of a unique framework to assess the impact of blended learning approaches from the viewpoint of student readiness and intensity of separate delivery approaches – whilst maintaining the need to evaluate the effectiveness of blended learning as an overall package.

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