Letteratura scientifica selezionata sul tema "Numeric traces and learning indicators"
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Articoli di riviste sul tema "Numeric traces and learning indicators":
Gregory, Peter, e Alan Lindsay. "Domain Model Acquisition in Domains with Action Costs". Proceedings of the International Conference on Automated Planning and Scheduling 26 (30 marzo 2016): 149–57. http://dx.doi.org/10.1609/icaps.v26i1.13762.
Batchakui, Bernabé, Thomas Djotio, Ibrahim Moukouop e Alex Ndouna. "Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments". International Journal of Emerging Technologies in Learning (iJET) 16, n. 21 (15 novembre 2021): 26. http://dx.doi.org/10.3991/ijet.v16i21.25033.
Jędrzejec, Bartosz, e Krzysztof Świder. "Automatically conducted learning from textually expressed vacationers’ opinions". ITM Web of Conferences 21 (2018): 00024. http://dx.doi.org/10.1051/itmconf/20182100024.
Ozerova, G. P. "Usage of Learning Management System Web Analytics in Blended Learning Self-Study Evaluation". Vysshee Obrazovanie v Rossii = Higher Education in Russia 29, n. 8-9 (9 settembre 2020): 117–26. http://dx.doi.org/10.31992/0869-3617-2020-29-8-9-117-126.
Yang, Linyi, Jiazheng Li, Ruihai Dong, Yue Zhang e Barry Smyth. "NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence 36, n. 10 (28 giugno 2022): 11604–12. http://dx.doi.org/10.1609/aaai.v36i10.21414.
Mohssine, Bentaib, Aitdaoud Mohammed, Namir Abdelwahed e Talbi Mohammed. "Adaptive Help System Based on Learners ‘Digital Traces’ and Learning Styles". International Journal of Emerging Technologies in Learning (iJET) 16, n. 10 (25 maggio 2021): 288. http://dx.doi.org/10.3991/ijet.v16i10.19839.
Juhaňák, Libor, Karla Brücknerová, Barbora Nekardová e Jiří Zounek. "Goal Setting and Goal Orientation as Predictors of Learning Satisfaction and Online Learning Behavior in Higher Education Blended Courses". Studia paedagogica 28, n. 3 (2 aprile 2024): 39–58. http://dx.doi.org/10.5817/sp2023-3-2.
Salihoun, Mohammed, Fatima Guerouate, Naoual Berbiche e Mohamed Sbihi. "How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum." International Journal of Emerging Technologies in Learning (iJET) 12, n. 03 (27 marzo 2017): 169. http://dx.doi.org/10.3991/ijet.v12i03.6506.
Yudha, Firma, e Alex Haris Fauzi. "Efektivitas Penggunaan Media Kartu Numerik pada Siswa Jenjang Prasekolah". Indonesian Journal of Mathematics and Natural Science Education 2, n. 1 (30 giugno 2021): 28–33. http://dx.doi.org/10.35719/mass.v2i1.56.
Martinez-Gil, Francisco, Miguel Lozano, Ignacio García-Fernández, Pau Romero, Dolors Serra e Rafael Sebastián. "Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations". Mathematics 8, n. 9 (2 settembre 2020): 1479. http://dx.doi.org/10.3390/math8091479.
Tesi sul tema "Numeric traces and learning indicators":
Ben, Soussia Amal. "Analyse prédictive des données d’apprentissage, en situation d’enseignement à distance". Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0216.
Over the past few decades, the adoption of e-learning has evolved rapidly and its use has been pushedeven further with the COVID-19 pandemic. The objective of this learning mode is to guarantee thecontinuity of the learning process. However, the online learning is facing several challenges, and themost widespread is the high failure rates among learners. This issue is due to many reasons such asthe heterogeneity of the learners and the diversity of their learning behaviors, their total autonomy, thelack and/or the inefficiency of the pedagogical provided follow-up. . .. Therefore, teachers need a systembased on analytical and intelligent methods allowing them an accurate and early prediction of at-risk offailure learners. This solution is commonly adopted in the state of the art. However, the work carried outdoes not respond to some particularities of the learning process (the continuity and evolution of learning,the diversity of learners and their total autonomy) and to some teachers expectations such as the alertgeneration.This thesis belongs to the field of learning analytics and uses the numeric traces of online learnersto design a predictive system (Early Warning Systems (EWS)) dedicated to teachers in online establish-ments. The objective of this EWS is to identify learners at risk as soon as possible in order to alertteachers about them. In order to achieve this objective, we have dealt with several sub-problems whichhave allowed us to elaborate four scientific contributions.We start by proposing an in-depth methodology based on the Machine Learning (ML) steps and thatallows the identification of four learning indicators among : performance, engagement, reactivity andregularity. This methodology also highlights the importance of temporal data for improving predictionperformance. In addition, this methodology allowed to define the model with the best ability to identifyat-risk learners.The 2nd contribution consists in proposing a temporal evaluation of the EWS using temporal metricswhich measure the precocity of the predictions and the stability of the system. From these two metrics,we study the trade-offs that exist between ML precision metrics and temporal metrics.Online learners are characterized by the diversity of their learning behaviors. Thus, an EWS shouldrespond to this diversity by ensuring an equitable functioning with the different learners profiles. Wepropose an evaluation methodology based on the identification of learner profiles and that uses a widespectrum of temporal and precision metrics.By using an EWS, teachers expect an alert generation. For this reason, we design an algorithm which,based on the results of the prediction, the temporal metrics and the notion of alert rules, proposes anautomatic method for alert generation. This algorithm targets mainly at-risk learners.The context of this thesis is the French National Center for Distance Education (CNED). In parti-cular, we use the numeric traces of k-12 learners enrolled during the 2017-2018 and 2018-2019 schoolyears
Ji, Min. "Exploiting activity traces and learners’ reports to support self-regulation in project-based learning". Thesis, Lyon, INSA, 2015. http://www.theses.fr/2015ISAL0032/document.
Project-based Learning (PBL) is a learner-oriented instructional method, which enables learners to carry out challenging and authentic projects by thorough investigations. PBL affords learners the opportunities to organize and plan the project, to collaborate with peers and to look for the resources and guidance to achieve the project goals. However, PBL is difficult to implement successfully because learners often lack of the self-regulation skills required to monitor, reflect, manage and assess their project activities and learning. Self-Regulated Learning (SRL) can train learners to gain these skills. However, most learning systems used in PBL focus on providing rich learning materials to the learners but rarely offer possibilities to monitor and analyze their project and learning processes. The main goal of this thesis is to support SRL during PBL situations. We propose a general architecture of Project-based Learning Management System (PBLMS), which help learners to understand how to regulate their learning activities during the projects. This general architecture integrates an existing Learning Management System (LMS) and two tools we propose: a reporting tool and a dynamic dashboard. The reporting tool enhances learners' reflective processes by leading them to describe their non-instrumented activities, their reflections and assessments on the project activities based on semi-structured sentences. The system can record automatically the activity traces of the users' interactions with the LMS, the reporting tool and the dashboard. These activity traces are merged with the self-reporting data so that indicators can be calculated basing on this entire information. The dynamic dashboard supports learners in creating customizable indicators. Learners can specify the data to take into account, the calculation and the visualization modes. We implemented this theoretical proposition with the development of the DDART (Dynamic Dashboard based on Activity and self-Reporting Traces) platform that integrates the reporting tool and the dynamic dashboard. To evaluate the proposition, we firstly test the ability of DDART to recreate a large sample of indicators that are proposed in existing researches about the analysis of activities, cognition, emotion and social network. Furthermore, an experiment was conducted to evaluate the usability and perceived utility of DDART. According to the results of this experiment, we found that DDART supports learners' reflections on the way they carry out the project and provides them with the opportunities to monitor their activities and learning, even if the indicator creation could be difficult for the novices
Libri sul tema "Numeric traces and learning indicators":
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research, a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research., a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research, a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research, a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research., a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research., a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research, a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Clifford, Adelman, e United States. Office of Educational Research and Improvement. Office of Research., a cura di. Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Signs & traces: Model indicators of college student learning in the disciplines. Washington, D.C: Office of Educational Research and Improvement, U.S. Dept. of Education, Office of Research, 1989.
Capitoli di libri sul tema "Numeric traces and learning indicators":
Chihab, Lamyaa, Abderrahim El Mhouti e Mohammed Massar. "Using Learning Analytics Techniques to Calculate Learner’s Interaction Indicators from Their Activity Traces Data". In Lecture Notes on Data Engineering and Communications Technologies, 504–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15191-0_48.
Tatzl, Dietmar. "Systemic Autonomy as an Educational Factor for Learners and Teachers". In 9. The Answer is Learner Autonomy: Issues in Language Teaching and Learning., 75–94. Candlin & Mynard ePublishing Limited, 2013. http://dx.doi.org/10.47908/9/4.
Nelken, David. "Global Social Indicators, Comparison, and Commensuration: A Case Study of COVID Rankings". In The Global Community Yearbook of International Law and Jurisprudence 2020, 995–1020. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197618721.003.0083.
Atti di convegni sul tema "Numeric traces and learning indicators":
Tuns, Adrian-Ioan, e Adrian Spătaru. "Cloud Service Failure Prediction on Google’s Borg Cluster Traces Using Traditional Machine Learning". In 2023 25th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2023. http://dx.doi.org/10.1109/synasc61333.2023.00029.
Djouad, Tarek, Alain Mille, Christophe Reffay e Mohammed Benmohammed. "A New Approach Based on Modelled Traces to Compute Collaborative and Individual Indicators Human Interaction". In 2010 IEEE 10th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2010. http://dx.doi.org/10.1109/icalt.2010.21.
Pantović, Danijela, e Nemanja Pantić. "THE FUTURE OF TOURISM REQUIRES AN ORIGIN: TRACES OF AN OLD CULTURAL POLICY IN VRNJAČKA BANJA". In Tourism International Scientific Conference Vrnjačka Banja - TISC. FACULTY OF HOTEL MANAGEMENT AND TOURISM IN VRNJAČKA BANJA UNIVERSITY OF KRAGUJEVAC, 2022. http://dx.doi.org/10.52370/tisc22179dp.