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Journal articles on the topic 'Portfolio-based learning'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Pitts, John. "Understand portfolio-based learning." Education for Primary Care 18, no. 3 (2007): 404–6. http://dx.doi.org/10.1080/14739879.2007.11493566.

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Aliyudin, Dedin, Kusrin Kusrin, and Yousef Bani Ahmad. "E-PORTFOLIO BASED LEARNING." JUDIKA (JURNAL PENDIDIKAN UNSIKA) 9, no. 1 (2021): 49–61. http://dx.doi.org/10.35706/judika.v9i1.3820.

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The present study aimed to investigate the response of e-portfolio on EFL students’ speaking classroom. This study conducted to examines whether e-portfolio has good effect on improving student speaking skill. This study conducted at one of the junior high school in Bandung in the academic year 2019/2020. Seven students participated in this study. The type of this study was qualitative research and its design was a case study. The data obtained through observation, interview, and documentation. The findings of the research showed that e-portfolio has a positive response from the students. It w

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Pan, Wei, Jide Li, and Xiaoqiang Li. "Portfolio Learning Based on Deep Learning." Future Internet 12, no. 11 (2020): 202. http://dx.doi.org/10.3390/fi12110202.

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Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an ima

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Han, Yingjie. "Machine Learning Based Portfolio Analysis." Highlights in Business, Economics and Management 44 (November 25, 2024): 135–47. http://dx.doi.org/10.54097/wc6epk11.

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This paper investigates the effectiveness of machine learning methods in predicting stock returns in China. In this paper, we use 16 liquidity, momentum, market risk and macro fundamentals indicators, as well as 112 interaction terms between indicators and macro variables with monthly stock excess returns for the lagged period from January 2000 to November 2015 as the sample set, train elasticity networks, gradient boosting, random forests, neural networks, and simple linear regression models, and based on the data of December 2015, predict the January 2016 returns, form portfolios based on th

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Menouer, Tarek, and Souheib Baarir. "Parallel Learning Portfolio-based solvers." Procedia Computer Science 108 (2017): 335–44. http://dx.doi.org/10.1016/j.procs.2017.05.140.

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Joshi, Mohit Kumar, Piyush Gupta, and Tejinder Singh. "Portfolio-based learning and assessment." Indian Pediatrics 52, no. 3 (2015): 231–35. http://dx.doi.org/10.1007/s13312-015-0613-2.

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Romero, Lucila, Milagros Gutierrez, and Laura Caliusco. "Learning path based-portfolio for personalized learning." Journal on Advances in Theoretical and Applied Informatics 3, no. 1 (2017): 62. http://dx.doi.org/10.26729/jadi.v3i1.2447.

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Nowadays, lot of human activities have been affected by the use of technology needed to change in order to adapted. Particularly, in education domain the changes that occurred were not enough to break the traditional schemas of teaching. In this way, more and more people claim for real remodeling. Mainly, one of the most important subject is the necessity to adapt the teaching process to the particular characteristics of students, in a king of personalized learning. This work presents a conceptual framework, which proposes learning path based-portfolio as a way to personalize education. This f

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Song, Changting. "Portfolio Optimization Based on Machine Learning." Advances in Economics, Management and Political Sciences 25, no. 1 (2023): 203–12. http://dx.doi.org/10.54254/2754-1169/25/20230500.

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Machine learning is utilized widely in portfolio prediction and optimization, which provides a more reliable results with mean variance optimization. Besides, socially responsible investment portfolio considering social, environment and governance becomes an emerging theme in academic and industrial circles. Basing on both of that, this study aims to use machine learning models to predict return of ESG stocks in American stocks market and mean variance optimization for creating portfolio. The consequence demonstrates SVR has less residual in prediction and maximum sharpe or minimum variance po

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Toms, B. "Portfolio Based Learning in General Practice." Quality and Safety in Health Care 4, no. 1 (1995): 66. http://dx.doi.org/10.1136/qshc.4.1.66.

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Leight, Joanne, Myung-Ah Lee, and Molly K. Hare. "A Web-based Learning Portfolio System." Journal of Physical Education, Recreation & Dance 78, no. 9 (2007): 45–48. http://dx.doi.org/10.1080/07303084.2007.10598097.

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11

Ingrassia, Antonina. "Portfolio-based learning in medical education." Advances in Psychiatric Treatment 19, no. 5 (2013): 329–36. http://dx.doi.org/10.1192/apt.bp.111.009480.

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SummaryThe use of portfolios has rapidly expanded in recent years and is now relevant to a number of different aspects of professional practice in medicine in general and psychiatry in particular, including training, appraisal and revalidation as well as continuing professional development. In this article I will examine the background of important changes and new trends in medical education on which the increasing use of portfolios is based, their potential value as learning and assessment tools, and some of the challenges and dilemmas associated with their use.

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12

Han, Jungmin Grace. "Korean Preservice Music Teachers’ Perceptions of Portfolio-based Teacher Education: A qualitative study." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 19 (2023): 581–91. http://dx.doi.org/10.22251/jlcci.2023.23.19.581.

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Objectives This qualitative study aimed to examine Korean preservice music teachers’ perceptions of portfo-lio-based teacher education. Methods A focus group interview along with an open-ended survey was executed with five preservice music teachers who were enrolled in the same portfolio-based music history education course at a graduate school of education in Seoul, Korea. The collected data were transcribed in Korean, translated it into English, and then in-ductively analyzed. Results The findings show as follows. First, all the participants have rarely experienced portfolio-base

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13

Marheny, Lukitasari, Hasan Rusdi, Sukri Akhmad, and Handhika Jeffry. "Developing student's metacognitive ability in science through project-based learning with e-portfolio." International Journal of Evaluation and Research in Education (IJERE) 10, no. 3 (2021): 948–55. https://doi.org/10.11591/ijere.v10i3.21370.

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The present study aimed to investigate the metacognitive ability of students using e-portfolio assessment in project-based learning classes. The projects were a set out a critical analysis based on the selected references (project 1) and set out of field activities based on the selected theme content (project 2). Student metacognitive ability consists of three phases, planning, implementation, and evaluation abilities that were assessed through e-portfolio assignments. There were 87 participants who divided into groups consisted of four to five students, conducted the projects, and submitted t

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Mansvelder-Longayroux, Desireé D., Douwe Beijaard, Nico Verloop, and Jan D. Vermunt. "Functions of the Learning Portfolio in Student Teachers’ Learning Process." Teachers College Record: The Voice of Scholarship in Education 109, no. 1 (2007): 126–59. http://dx.doi.org/10.1177/016146810710900103.

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In this study, we aimed to develop a framework that could be used to describe the value of the learning portfolio for the learning process of individual student teachers. Retrospective interviews with 21 student teachers were used, as were their portfolio-evaluation reports on their experiences of working on a portfolio. Seven functions of the learning portfolio in the student teachers’ learning process emerged from the data. It was possible to distinguish between product and process functions: with product functions, the production of a portfolio was seen as working on a tangible end product;

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Li, Li, and Kun Ling Wang. "The Construction of E-Portfolio Based Web Autonomous Learning Environment." Advanced Materials Research 219-220 (March 2011): 18–21. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.18.

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Web-based autonomous learning environment is the circumstances and conditions in the process of learning activities with the introduction of the network factors, its basic elements not only include the support of courses contents, instructional strategies, teaching mode and resources, but also cover instruction design, management, evaluation and other areas. The e-Portfolio (Electronic Learning Portfolio) is a collection of the learner’s experiences and achievements during a period of educational activity, which has advantages in dynamic tracking, recording, collection, evaluation ,management

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Juita, Ratna. "Peningkatan Kemampuan Menulis Laporan dengan Model Pembelajaran Berbasis Portofolio." Jurnal Kajian Bahasa, Sastra dan Pengajaran (KIBASP) 2, no. 1 (2018): 1–10. http://dx.doi.org/10.31539/kibasp.v2i1.327.

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This study aimed to (1) describe the learning outcome of writing a report before a portfolio-based learning model of the students of class VIII B at MTs Negeri 2 Bengkulu was conducted, (2) describe the result of the learning model in writing a report after portfolio-based learning in class VIII B MTs Negeri 2 Bengkulu was conducted, (3) describe the improvement in the ability to write a report based on the portfolio-based learning model of the students of class VIII B at MTs Negeri 2 Bengkulu. (1) The research was an action research, with descriptive analysis technique. The results of this st

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Hughes, Julie. "E-portfolio-based learning: a practitioner perspective." Enhancing Learning in the Social Sciences 1, no. 2 (2008): 1–12. http://dx.doi.org/10.11120/elss.2008.01020005.

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18

Chen, Shun, and Lei Ge. "A learning-based strategy for portfolio selection." International Review of Economics & Finance 71 (January 2021): 936–42. http://dx.doi.org/10.1016/j.iref.2020.07.010.

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19

Alan Hung, Shao-Ting, and Heng-Tsung Danny Huang. "E-portfolio-based Language Learning and Assessment." International Journal of Learning: Annual Review 17, no. 7 (2010): 313–36. http://dx.doi.org/10.18848/1447-9494/cgp/v17i07/46886.

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20

Šliogerienė, Jolita. "Students’ Reflection Analysis in Portfolio – Based Learning." Santalka 20, no. 1 (2012): 98–105. http://dx.doi.org/10.3846/cpe.2012.10.

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21

吕, 林黛. "Intelligent Portfolio Optimization Based on Machine Learning." Computer Science and Application 13, no. 03 (2023): 349–57. http://dx.doi.org/10.12677/csa.2023.133033.

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22

Jartarghar, Uday, Prakash Hegade, and Ashok Shettar. "Portfolio Assessments for Problem Based Learning Courses." Journal of Engineering Education Transformations 38, IS2 (2025): 252–59. https://doi.org/10.16920/jeet/2025/v38is2/25030.

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Problem-Based Learning is a student-centered teaching and learning pedagogy proven to be effective and applicable for wide range of domains. Problem based learning supports several forms of assessments like project work, casestudies, and reflections, all of them with the objective to improve problem solving, critical thinking and self-directed learning. Portfolios can be yet another way of supporting and designing assessments. This study proposes a research question to understand the impact of portfolio creation as an assessment method within Problem-Based Learning pedagogy and assesses its im

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Akhilesh, Kumar, and Shahid Mohammad. "Portfolio selection model using teaching learning-based optimization approach." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1083–90. https://doi.org/10.11591/ijai.v12.i3.pp1083-1090.

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Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem (PSP). However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization prob

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24

Laksana, I. Putu Yoga, I. Gusti Ayu Putu Dewi Paramita, Ni Nyoman Yuliantini, and Ni Wayan Sadiyani. "The Student’s Perception in Using Trello as A Learning Media to Support E-Portfolio Based Learning." Prosiding Seminar Nasional Riset Bahasa dan Pengajaran Bahasa 5, no. 1 (2023): 119–27. http://dx.doi.org/10.31940/senarilip.v5i1.119-127.

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This study aims at evaluating students’ perceptions of using the Trello application as a learning media to support e-portfolio based learning. The research employs a survey method to assess students’ perspectives on the usefulness and ease of use of Trello for organizing course materials, managing assignments, and developing their digital portfolios within a blended learning environment. The participants are the students of International Business Management study program who are following the English for Specific Couse named English for Business which implementing Trello-supported e-portfolio

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25

Elango, S., RC Jutti, and LK Lee. "Portfolio as a Learning Tool: Students’ Perspective." Annals of the Academy of Medicine, Singapore 34, no. 8 (2005): 511–14. http://dx.doi.org/10.47102/annals-acadmedsg.v34n8p511.

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Portfolio writing is a method of encouraging reflective learning among professionals. Although portfolio-based learning is popular among educators, not many studies have been done to determine students’ perceptions of portfolio as a learning tool. A questionnaire survey was conducted among 143 medical students to find out their perceptions of the portfolio as a learning tool. A majority of the students felt that the portfolio is a good learning tool. However, they also perceived that it is stressful and time-consuming to develop a proper portfolio. The study indicates that students need approp

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Ulfa, Jufiani, Endang Aldilla, Fatni Mufit, and Festiyed Festiyed. "THE INFLUENCE OF IMPLEMENTING PORTFOLIO ASSESSMENTS IN SCIENCE LEARNING ON STUDENT LEARNING OUTCOMES: A SYSTEMATIC REVIEW." EduFisika: Jurnal Pendidikan Fisika 8, no. 3 (2023): 268–81. http://dx.doi.org/10.59052/edufisika.v8i3.28677.

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This literature review aims to investigate how the portfolio assessment used in research influences the learning outcomes of students based on the objectives of the analysis, research design used, level of education, learning topic, and form of an assessment tool. The most significant benefit of portfolio assessment was obtained in applied research/influence/effectiveness, namely 61.54%. Science learning with a research design had the highest frequency in a quasi-experimental design of 42.31%. The level of education was another factor that affected the portfolio assessment of science learning.

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Raushan, Ritik, Sangam Kumari, Sanskriti Singh, Pawan Mishra, Dr Vikas Singhal, and Dr Ajay Sahu. "AI Based Course Suggestion Portfolio." International Journal of Innovative Research in Advanced Engineering 11, no. 12 (2024): 847–53. https://doi.org/10.26562/ijirae.2024.v1112.03.

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The rapid expansion of online education platforms has fundamentally changed the way students engage with learning opportunities. Students now have access to a vast array of courses, offered by different institutions, in various fields of study, and at different levels. [1] However, with such an overwhelming selection, choosing the right courses that align with a student's academic path, interests, and career aspirations can be a daunting task. Traditional course recommendation systems, which have relied on simple rule-based or manual methods, often fall short of addressing the diverse and evol

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Balcan, Maria-Florina, Tuomas Sandholm, and Ellen Vitercik. "Generalization in Portfolio-Based Algorithm Selection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12225–32. http://dx.doi.org/10.1609/aaai.v35i14.17451.

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Portfolio-based algorithm selection has seen tremendous practical success over the past two decades. This algorithm configuration procedure works by first selecting a portfolio of diverse algorithm parameter settings, and then, on a given problem instance, using an algorithm selector to choose a parameter setting from the portfolio with strong predicted performance. Oftentimes, both the portfolio and the algorithm selector are chosen using a training set of typical problem instances from the application domain at hand. In this paper, we provide the first provable guarantees for portfolio-based

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Köpeczi-Bócz, Tamás. "Learning Portfolio and Proactive Learning in Higher Education Pedagogy." International Journal of Engineering Pedagogy (iJEP) 10, no. 5 (2020): 34. http://dx.doi.org/10.3991/ijep.v10i5.13793.

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Industry 4.0 requires highly skilled, sophisticated workers and entrepreneurs, and continuous training is essen-tial for them, and its scene is increasingly shifting to higher education. For this reason, higher education peda-gogy must go through continuous innovation. In this article, we present a model experiment of VET teacher training, which consists of a so-called proactive learning method and a portfolio-based evaluation process. In the pedagogical model, the student-teacher relationship focuses on the preparation process before the classroom meeting. During proactive learning, the stude

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Kamal, Muhiddinur, Muhammad Zainuddin bin Arrifaldi, and Muhamad Rezi. "Fostering Students’ Creativity in Academic Writing at Islamic Junior High School Koto Tuo, West Pasaman." Al-Ta lim Journal 28, no. 1 (2021): 35–45. http://dx.doi.org/10.15548/jt.v28i1.659.

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The current study aimed at 1) developing a portfolio-based learning model at Madrasah Tsanawiyah, and 2) testing the validity, practicality, and effectiveness of the developed learning model. The design of the study was Research and Development (RD) using a 4-D model (defines, design, develop, and disseminate). Teachers’ books, students’ workbooks, a learning model, and validity, reliability, and practicality test were designed to support the implementation of the learning model to the teaching and learning process in the classroom. The qualitative data were collected through observation, inte

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Vitalii, Martovytskyi, Argunov Volodymyr, Ruban Igor, and Romanenkov Yuri. "Developing a risk management approach based on reinforcement training in the formation of an investment portfolio." Eastern-European Journal of Enterprise Technologies 2, no. 3(122) (2023): 106–16. https://doi.org/10.15587/1729-4061.2023.277997.

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Investments play a significant role in the functioning and development of the economy. Risk management is an integral part of the formation of the investment portfolio. This means that an investor must be willing to take on a certain level of risk in order to receive a certain level of return. However, when forming an investment portfolio, an investor faces such problems as market unpredictability, asset correlation, incorrect asset allocation. Therefore, when forming an investment portfolio, an investor should carefully study all possible risks and try to minimize them. The object of research

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Snadden, D., M. L. Thomas, E. M. Griffin, and H. Hudson. "Portfolio-based learning and general practice vocational training." Medical Education 30, no. 2 (1996): 148–52. http://dx.doi.org/10.1111/j.1365-2923.1996.tb00733.x.

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胥, 智星. "Portfolio Management Problem Based on Deep Reinforcement Learning." Operations Research and Fuzziology 13, no. 03 (2023): 1427–41. http://dx.doi.org/10.12677/orf.2023.133144.

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Zhang, Erchuan. "Portfolio Optimization Strategy Based on Four Deep Learning Models." Advances in Economics, Management and Political Sciences 47, no. 1 (2023): 295–302. http://dx.doi.org/10.54254/2754-1169/47/20230402.

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Deep learning techniques have provided a fresh outlook on the evergreen subject of portfolio optimization within the finance domain. This article selects the stocks of Google, Tesla, Tractor Supply Company, Analog Devices, and Duke Energy Corporation and deploys four deep learning models to estimate returns and covariance respectively. The mean-variance model is utilized to generate the target portfolio for each deep learning model, incorporating the predicted outcomes. Ultimately, the returns of each portfolio are compared to the market benchmark (S&P 500) returns. The findings demonstrat

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Daname, KOLANI, and BENBACHIR Saad. "Portfolio selection based on predictive approach." African scientific journal Vol 3, N° 11 (2022): 326. https://doi.org/10.5281/zenodo.6537475.

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<strong>Abstract</strong> The paper analyzes a conceptual value investor behavior that uses stock fundamentals to predict the best-performing portfolio according to the knowledge it has at its disposal from previous periods. A class of machine learning algorithms based on inductive learning is used to learn from past data and predict by induced rules which stock to select for the final portfolio. This approach has led not only to corroborate with other research on the relevance of financial fundamentals to explain the future returns of stock but also to design a strategy th

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Ma, Jiong. "E-Portfolio: The Research for Similarities and Differences." Applied Mechanics and Materials 380-384 (August 2013): 4632–36. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4632.

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With the rapid development of Internet. Web-based Learning became more convenient and rapid, People-centered Web-based Learning become possible now. Electronic Portfolio is such kind of a platform that to adapt the Web-based Learning for users to share, exchange, collaborative learning and assessment. The paper started from the concept and feature of Electronic Portfolio, and introduced some commonly used alternative platform for Electronic Portfolio. Compared and analyzed it from the angle of functions, features, support technology, users, storage and assessment.

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Noh, Yoochan. "Diversified portfolio optimization under deep learning prediction and heuristics optimization." International Journal of Engineering Research and Applications 14, no. 7 (2024): 127–37. http://dx.doi.org/10.9790/9622-1407127137.

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In the financial market volatility and uncertainty are the biggest challenges to the proper handling of the portfolio of stocks. Much research has been done on portfolio optimization using a meta-heuristic approach. However, the difficulty still lies in the allocation of stocks in the portfolio to simultaneously maximize the return and minimize risk in diversified stock allocation. In this study, a diversified portfolio with 10 stocks is randomly considered from the semiconductor industry, car industry, pharmaceuticals, retail chain, and service industry. Since the stocks are from different se

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Jeong, Jae Wook, and Doguk Kim. "Portfolio Optimization Using Reinforcement Learning with Transformer-Based Policy." Korean Institute of Smart Media 14, no. 4 (2025): 56–66. https://doi.org/10.30693/smj.2025.14.4.56.

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Traditional portfolio optimization has had a significant impact on academia and practice. However, this approach requires several constraints to be applicable in real-world markets. With the rapid advancements in artificial intelligence, research employing AI for portfolio optimization has become increasingly active, and tools such as FinRL are now widely utilized, with the EIIE model demonstrating stable performance. Nevertheless, this model faces structural limitations in capturing asset classes and temporal information within a portfolio. To address these limitations, this study introduces

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Jain, Prayut, and Shashi Jain. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification." Risks 7, no. 3 (2019): 74. http://dx.doi.org/10.3390/risks7030074.

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The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk-based allocation methods, HRP is also a function of the estimate of the covariance matrix, however, it does not require its invertibility. In this paper, we first study the impact of covariance misspecification on the performance of the different allocation methods. Next, we study under an appropriate covariance forecast model whether the machine learning based HRP outperforms the traditio

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Kumar, Akhilesh, and Mohammad Shahid. "Portfolio selection model using teaching learning-based optimization approach." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1083. http://dx.doi.org/10.11591/ijai.v12.i3.pp1083-1090.

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<p>Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem. However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization p

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Jayanto, Nur, Shamal Kaveh, and Gleen A. Davies. "The Influence of Application of Portfolio-Based Learning Model on Student Learning Outcomes in History Class XI." Journal of Social Knowledge Education (JSKE) 4, no. 3 (2023): 92–102. http://dx.doi.org/10.37251/jske.v4i3.700.

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Purpose of the study: This research was conducted with the aim of knowing the effect of applying a portfolio-based learning model on student learning outcomes in history class XISMAN Negeri 1 Kembang in the 2012-2013 academic year. Methodology: This type of research is experimental research. The experimental design used is the Pre test-Post test. The population in this study were all students of class XI IPS at SMA N 1 Kembang in the academic year 2012/2013, totaling 177 students. Sampling was carried out using random sampling so that researchers got XIIPS2 class as the experimental clas

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Magdalena, Ina, Savina Nurrahmadanti Ramdhan, and Vivi Luftiyah. "Analisis Penilaian Berbasis Fortofolio pada Pembelajaran Tematik Kelas 3 di Sekolah Dasar Negeri Batujaya Kecamatan Batu Ceper." ARZUSIN 1, no. 1 (2021): 97–113. http://dx.doi.org/10.58578/arzusin.v1i1.111.

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This research was conducted to describe the Analysis of Fortofolio Based Assessment in Grade 3 Thematic Learning as an assessment and evaluation tool for the learning process of students. One of the advantages of portfolio assessment is that it provides opportunities for students to be more involved, and students themselves can more easily control the extent to which the development of abilities they acquire. In the world of education, teachers can use portfolios to see the progress of students from time to time based on a collection of work as evidence of a learning activity. This Portfolio A

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Lyu, Xuanlin. "Portfolio Optimization Strategies: New Approaches Based on Machine Learning Forecasting." Highlights in Business, Economics and Management 40 (September 1, 2024): 1077–82. http://dx.doi.org/10.54097/cpgtg807.

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This study provides an in-depth discussion and comprehensive review of the latest applications of machine learning techniques in the field of portfolio optimization. The article begins with an overview of traditional portfolio optimization theory and its limitations, and then focuses on how machine learning predictive models, which have flourished in recent years, can provide new perspectives and tools for solving the problems of non-linearity, dynamics and uncertainty in investment decision-making. This paper provides a detailed overview of the application practices of various machine learnin

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Mohamad, Syamsul Nor Azlan, Siti Nur Dini Mahazan, and Siti Zuraida Maaruf. "Authentic assessment in Studio-Based Learning (SBL) e-Portfolio." International Journal of e-Learning and Higher Education 14, no. 1 (2021): 5–18. http://dx.doi.org/10.24191/ijelhe.v14n1.1411.

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This article takes an in-depth look at the rationale behind the integrating e-Portfolio as an authentic assessment in studio-based learning in higher education institutions (HEIs) which could impact the performance of the studio learners in professional development. In the field of studio based learning (SBL), the studio learning system is comprehensive as it combines theoretical and practical knowledge which covers various disciplines. Learning in the studio field through a complex iterative process involves collecting, processing, analysis, translation, synthesis, design and delivery of a pr

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Vonti, Lungguh Halira. "The Relationship Between Learning Motivation, Portfolio Based Assessment, and Students Writing Ability." Pedagogia: Jurnal Ilmiah Pendidikan 8, no. 2 (2016): 424–29. http://dx.doi.org/10.55215/pedagogia.v8i2.4922.

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The study is aimed to reveal the relationship between learning motivation, portfolio based assessment with the students writing ability. The study was conducted in second semester of English Education Study Program in Faculty of Teachers Training and Educational Sciences, Pakuan University. The population of the study is the second semester of the English Department, Faculty of Teachers Training and Educational Sciences, Pakuan University. From the population, the sample of the study is two classes with 56 students. The method of the study is correlative study with the technique of data analys

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DOBRINA, M. V., and V. P. CHERNOV. "OPTIMIZATION AND FORECASTING OF A SECURITIES PORTFOLIO BASED ON MACHINE LEARNING METHODS." Economic Problems and Legal Practice 20, no. 4 (2024): 258–67. http://dx.doi.org/10.33693/2541-8025-2024-20-4-258-267.

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The article is devoted to the demonstration and analysis of the possibilities of connecting the Markowitz model, which is a classical model for forming a portfolio of securities, and forecasting methods based on machine learning data. The relevance of the chosen topic is justified by the fact that the use of machine learning methods makes it possible to simplify the activities of financial sector workers and the work of professional stock market players, as well as reduce the complexity of interpreting mathematical models and methods of their construction for private investors. The purpose of

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Nastiti, Defika Putri, Sigit Santoso, and Sudiyanto Sudiyanto. "The Effectiveness of Using the Portfolio Assessment Model to Improve Student Self-Regulated in Economic Learning." International Journal of Multicultural and Multireligious Understanding 9, no. 2 (2022): 424. http://dx.doi.org/10.18415/ijmmu.v9i2.3484.

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The study aimed to determine: (1) the differences in the self-regulated of students in economics learning using a portfolio assessment model and without using a portfolio assessment model, and (2) the effectiveness of using a portfolio assessment model on students' self-regulated in economics learning. This research is a quasi-experimental study with a randomized control group pretest-posttest design. The population of this research is class XI students majoring in Social Sciences (IIS) for the academic year 2021/2022 which consists of four classes with a sampling technique, namely cluster ran

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Sukhija, Nitin, Brandon Malone, Srishti Srivastava, Ioana Banicescu, and Florina M. Ciorba. "A Learning-based Selection for Portfolio Scheduling of Scientific Applications on Heterogeneous Computing Systems." Parallel & Cloud Computing 3, no. 4 (2014): 66–81. http://dx.doi.org/10.14511/pcc.2014.030401.

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Piao, Jinze. "Portfolio Optimization Based on Deep Learning and Factor Constraints." Advances in Economics, Management and Political Sciences 48, no. 1 (2023): 264–73. http://dx.doi.org/10.54254/2754-1169/48/20230454.

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Portfolio construction can help investors achieve a balance between risks and returns, and rationally allocate funds to maximize returns. This paper combines four deep learning models LSTM, GRU, CRNN, TCN with investment portfolio strategies Mean-Variance, Mean-CVaR, selects stock data from different industries in the US stock market to construct portfolios, and these strategies are tested in both bull and bear market environment. Comparative analysis of cumulative returns reveals that in the bull market, the cumulative return of TCN+MV and GRU+MV is the highest. In the bear market, the cumula

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Dong, Lyucheng. "LSTM-based Portfolio Optimization Strategy for SP500." Advances in Economics, Management and Political Sciences 25, no. 1 (2023): 194–202. http://dx.doi.org/10.54254/2754-1169/25/20230499.

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Portfolio optimization is a perennial topic in the field of finance and recent breakthrough in deep learning techniques offers a new perspective to tackle it. This study selects 30 stocks of SP500 in different sectors through various constraints and deploys Long Short-Term Memory and Ledoit-Wolf Shrinkage to estimate returns and covariance respectively. The target portfolio is then obtained by inputting the predicted results into the mean-variance model, which is dynamically updated on a daily basis given evolving market information. The results show that the target model this study proposed s

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