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Статті в журналах з теми "Numeric traces and learning indicators":

1

Gregory, Peter, and Alan Lindsay. "Domain Model Acquisition in Domains with Action Costs." Proceedings of the International Conference on Automated Planning and Scheduling 26 (March 30, 2016): 149–57. http://dx.doi.org/10.1609/icaps.v26i1.13762.

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This paper addresses the challenge of automated numeric domain model acquisition from observations. Many industrial and commercial applications of planning technology rely on numeric planning models. For example, in the area of autonomous systems and robotics, an autonomous robot often has to reason about its position in space, power levels and storage capacities. It is essential for these models to be easy to construct. Ideally, they should be automatically constructed. Learning the structure of planning domains from observations of action traces has produced successful results in classical planning. In this work, we present the first results in generalising approaches from classical planning to numeric planning. We restrict the numeric domains to those that include fixed action costs. Taking the finite state automata generated by the LOCM family of algorithms, we learn costs associated with machines; specifically to the object transitions and the state parameters. We learn action costs from action traces (with only the final cost of the plans as extra information) using a constraint programming approach. We demonstrate the effectiveness of this approach on standard benchmarks.
2

Batchakui, Bernabé, Thomas Djotio, Ibrahim Moukouop, and Alex Ndouna. "Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments." International Journal of Emerging Technologies in Learning (iJET) 16, no. 21 (November 15, 2021): 26. http://dx.doi.org/10.3991/ijet.v16i21.25033.

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This paper proposes a traces model in the form of an object or class model (in the UML sense) which allows the automatic calculation of indicators of various kinds and independently of the computer environment for human learning (CEHL). The model is based on the establishment of a trace-based system that encompasses all the logic of traces collecting and indicators calculation. It is im-plemented in the form of a trace database. It is an important contribution in the field of the exploitation of the traces of apprenticeship in a CEHL because it pro-vides a general formalism for modeling the traces and allowing the calculation of several indicators at the same time. Also, with the inclusion of calculated indica-tors as potential learning traces, our model provides a formalism for classifying the various indicators in the form of inheritance relationships, which promotes the reuse of indicators already calculated. Economically, the model can allow organi-zations with different learning platforms to invest only in one traces Management System. At the social level, it can allow a better sharing of trace databases be-tween the various research institutions in the field of CEHL.
3

Jędrzejec, Bartosz, and 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.

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The automatically conducted consumers’ opinions investigation is one of the most interesting potential applications of text analytics. In our study we perform a two steps procedure of learning from the textually expressed reviews concerning hotel services offered by a travel company. In the first stage we accomplish the necessary Extract-Transform-Load process utilizing one of the available web portals and required language resources. In the second stage each of the suitably pre-processed opinions is “linguistically evaluated”, which results in a vector of numeric indicators characterizing its sentiment.
4

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, no. 8-9 (September 9, 2020): 117–26. http://dx.doi.org/10.31992/0869-3617-2020-29-8-9-117-126.

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Learning Management System (LMS) analytics data is proposed to be used in developing algorithms for evaluating students’ self-studies. Development of such algorithms is relevant considering annual growth of disciplines that apply blended learning. In blended learning model selfstudy can be done online in LMS which makes it possible to analyze patterns how students interact with learning materials and perform exercises of various complexity. Different criteria and indicators are aggregated into numeric metrics that following designed methodology evaluates self-study performance of each student. Designed methodology uses algorithms that evaluate self-study results by using empirical LMS analytics data. Developed algorithms allow us on one hand to interpret empirical data for self-studies evaluation, and on the other hand to correct and improve students’ learning path. This paper presents results of using developed methodology deployed in LMS BlackBoard on the example of Information Technology blended learning course in Far Eastern Federal University.
5

Yang, Linyi, Jiazheng Li, Ruihai Dong, Yue Zhang, and Barry Smyth. "NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11604–12. http://dx.doi.org/10.1609/aaai.v36i10.21414.

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Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context.
6

Mohssine, Bentaib, Aitdaoud Mohammed, Namir Abdelwahed, and Talbi Mohammed. "Adaptive Help System Based on Learners ‘Digital Traces’ and Learning Styles." International Journal of Emerging Technologies in Learning (iJET) 16, no. 10 (May 25, 2021): 288. http://dx.doi.org/10.3991/ijet.v16i10.19839.

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Learning management system (LMS) such as Claroline, Ganesha, Chamilo, Moodle ..., are commonly and well used in e-education (e-learning). Most of theTechnology Enhanced Learning (TEL) focus on supporting teachers in the creation and organization of online courses. However, in general, they do not consider individual differences of each learner. In addition, they do not provide enough indicators which will help to track the learners. In this paper, we investigate the benefits of integrating learning styles in the Web-based educational systems. Also we are interested in the use of interaction traces in order to address the lack of feedback between the learner and the teacher. Generally, we aim to offer a tool that allows the tutor and the instructional designer to interpret learner courses, in order to provide help as needed for each individual.
7

Juhaňák, Libor, Karla Brücknerová, Barbora Nekardová, and Jiří Zounek. "Goal Setting and Goal Orientation as Predictors of Learning Satisfaction and Online Learning Behavior in Higher Education Blended Courses." Studia paedagogica 28, no. 3 (April 2, 2024): 39–58. http://dx.doi.org/10.5817/sp2023-3-2.

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This study investigated how goal setting and goal orientation are related to student learning behavior and engagement in an online learning environment, and how learning behavior, goal setting, and goal orientation are related to student satisfaction with the course they are studying. A total of 882 students from 76 different courses participated in this study, which used both self-reported data from a questionnaire and indicators based on digital traces in an online learning environment. The results of multilevel regression analyses showed that student ability to set learning goals (i.e., goal setting) was positively related to both student learning satisfaction and student learning behavior. Intrinsic goal orientation positively predicted student satisfaction with the course. Extrinsic goal orientation did not show a significant effect in any of the observed relationships. The analyzed indicators of student learning behavior showed no statistically significant association with learning satisfaction. Possible explanations for these findings are discussed, and limitations and directions for future research are suggested.
8

Salihoun, Mohammed, Fatima Guerouate, Naoual Berbiche, and 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, no. 03 (March 27, 2017): 169. http://dx.doi.org/10.3991/ijet.v12i03.6506.

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Computer Supported Collaborative Learning (CSCL) is a new mode of teaching and one of the popular approaches for learning process. It allows virtual interactions between groups by providing tools such as: chat, internal email and discussion forums. One of the major problems caused by this learning process is the neglect and isolation of learners in groups, and usually is the cause of a heterogeneous group through social, cognitive or emotional ways. The method used is based on the exploitation of traces left on the online learning platform by learners and groups. The data collected from the environment can be observed and exploited in order to build social and cognitive indicators. Our approach is to design a model which assists the tutor to rebuild groups who are not homogeneous in order to prevent their isolation and abandonment. Our model offers the tutor the opportunity to rebuild the groups in an automatic way and based on the characteristics of quantitative indicators of all learners. Our work allowed us to test our algorithm from a functional and technical point of view and also identifies real variables from a collaborative online learning. It also allowed us to evaluate six different indicators proposed for this experiment, showing that they may assist the tutor to rebuild many groups again. The results show us that after the rebuilding groups, there has been a lot of participation in the forum and a considerable number of shares and documents deposited to the forum for each group. This high frequency of interaction between learners, lead them to a fruitful collaboration, and a good quality work at the end. The integration of other more advanced indicators may provide to tutor a better visibility to rebuild the groups that face difficulties.
9

Yudha, Firma, and Alex Haris Fauzi. "Efektivitas Penggunaan Media Kartu Numerik pada Siswa Jenjang Prasekolah." Indonesian Journal of Mathematics and Natural Science Education 2, no. 1 (June 30, 2021): 28–33. http://dx.doi.org/10.35719/mass.v2i1.56.

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Abstrak Pembelajaran di jenjang prasekolah lebih banyak bermain sambil belajar. Pembelajaran matematika juga dapat diterima anak dalam dunia penddikan pra sekolah, yaitu dengan cara mengenal numerik 1-10. Hal demikian dilakukan dengan menggunakan media kartu numerik secara klasikal. Jenis penelitian ini adalah deskriptif kualitatif dengan subjek penelitian anak-anak pra sekolah dhiva school dengan jumlah 18 anak. Dalam penelitian ini diketahui bahwa siswa pra sekolah mampu mengenal numerik, mampu menulis dan mampu mengurutkan numerik 1-10. efektifitas media kartu numerik, dengan menggunakan 4 indikator hasilnya untuk indikator kemauan belajar siswa kategori sangat baik, dengan rata-rata 84,4. Untuk indikator keterampilan siswa juga kriteria sangat baik, dengan skor rata-rata 83,27. Indikator berikutnya mengenal angka 1-10 dengan benar dan tepat juga memproleh kriteria sangat baik, dengan rata-rata 83. Indikator yang terakhir diperoleh skor rata-rata 80,94 jika dibulatkan menjadi 81, jadi untuk indikator ini memperoleh kriteria sangat baik. Abstract Their experiences in preschool more level playing while learning. Math lessons are also a possible child in pre-world education, school namely through numerical know 1-10. It thereby conducted using the card classical. The purpose of this study was to describe the effectiveness of numeric card media for pre-school level students in terms of attitudes and abilities in understanding the material.Numerically kind of research this is the qualitative descriptive subject of study children preschool Dhiva school of 18. Children in this research are that students are, numerical know schools can pre and capable of writing. 1-10 numeric rank The effectiveness of media, numerical card using the 4 indicators for the student learning a very good, category with an average 84,4. To the student skills also excellent, criteria with the average score 83,27. Know the following indicators 1-10 correctly and precise criteria, get very well with an average of 83. The last average score obtained 80,94 if rounded into 81, so this question received excellent. So that the introduction of numeric card media for pre-school students is very effective in increasing the ability of students to learn to count (numbers).
10

Martinez-Gil, Francisco, Miguel Lozano, Ignacio García-Fernández, Pau Romero, Dolors Serra, and Rafael Sebastián. "Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations." Mathematics 8, no. 9 (September 2, 2020): 1479. http://dx.doi.org/10.3390/math8091479.

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Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table or a function approximator. The learned behavior is then derived using a greedy policy with respect to this value function. Nevertheless, sometimes the learned policy does not meet expectations, and the task of authoring is difficult and unsafe because the modification of one value or parameter in the learned value function has unpredictable consequences in the space of the policies it represents. This invalidates direct manipulation of the learned value function as a method to modify the derived behaviors. In this paper, we propose the use of Inverse Reinforcement Learning to incorporate real behavior traces in the learning process to shape the learned behaviors, thus increasing their trustworthiness (in terms of conformance to reality). To do so, we adapt the Inverse Reinforcement Learning framework to the navigation problem domain. Specifically, we use Soft Q-learning, an algorithm based on the maximum causal entropy principle, with MARL-Ped (a Reinforcement Learning-based pedestrian simulator) to include information from trajectories of real pedestrians in the process of learning how to navigate inside a virtual 3D space that represents the real environment. A comparison with the behaviors learned using a Reinforcement Learning classic algorithm (Sarsa(λ)) shows that the Inverse Reinforcement Learning behaviors adjust significantly better to the real trajectories.
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Дисертації з теми "Numeric traces and learning indicators":

1

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.

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Pendant les dernières décennies, l'adoption de l'apprentissage en ligne a rapidement évolué et son utilisation a été encore plus poussée avec la pandémie de la COVID-19. L'objectif de ce mode d'enseignement est de garantir la continuité du processus d'apprentissage. Cependant, ce mode d'apprentissage connaît plusieurs défis, dont le plus répandu est les taux élevés d'échec. Ce problème est dû à de nombreuses raisons comme l'hétérogénéité des apprenants et la diversité de leurs comportements d'apprentissage, leur totale autonomie, le manque et/ou l'inefficacité du suivi pédagogique fourni. Par conséquent, les enseignants ont besoin d'un système basée sur des méthodes analytiques et intelligentes leur permettant une prédiction précise et au plus tôt des apprenants à risque d'échec. Cette solution est communément adaptée dans l'état de l'art. Cependant, les travaux réalisés ne répondent pas à certaines particularités de l'apprentissage (la continuité et l'évolution de l'apprentissage, la diversité des apprenants et leur totale autonomie) et certaines attentes des enseignants comme la génération d'alerte. Cette thèse s'inscrit dans le domaine de l'analyse de l'apprentissage et exploite les traces numériques des apprenants en ligne pour concevoir un système prédictif (Early Warning Systems (EWS)) dédié aux enseignants des établissements en ligne. L'objectif de ce EWS est d'identifier au plus tôt les apprenants à risque pour alerter les enseignants de ces derniers. Afin d'atteindre cet objectif, nous avons traité plusieurs sous-problématiques qui ont permis l'élaboration de quatre contributions scientifiques. Nous commençons par proposer une méthodologie en profondeur qui repose sur les étapes de l'apprentissage automatique (ML) et qui permet l'identification de quatre indicateurs d'apprentissage parmi : la performance, l'engagement, la réactivité et la régularité. Cette méthodologie met aussi en valeur l'importance des données temporelles pour l'amélioration des performances de prédiction. De plus, cette méthodologie a permis de définir le modèle avec la meilleure capacité à identifier les apprenants à risque. La 2ème contribution consiste à proposer une évaluation temporelle des EWS à l'aide des métriques temporelles qui mesurent la précocité des prédictions et la stabilité des systèmes. À partir de ces deux métriques, nous étudions les compromis qui existent entre les métriques de précision de ML et les métriques temporelles. Les apprenants en ligne se caractérisent par la diversité de comportements d'apprentissage. Ainsi, un EWS doit répondre à cette diversité en assurant un fonctionnement équitable entre les différents profils d'apprenants. Nous proposons une méthodologie d'évaluation qui se base sur l'identification des profils d'apprenants et utilise un large spectre de métriques temporelles et de précision. En utilisant un EWS, les enseignants s'attendent à une génération d'alerte. C'est pour cette raison,nous concevons un algorithme qui s'appuie sur les résultats de prédiction, les métriques temporelles et la notion des règles d'alerte pour proposer une méthode automatique de génération d'alerte. Le contexte applicatif de cette thèse est le Centre National d'Enseignement à Distance (CNED). Nous exploitons les traces numériques d'une population de collégiens inscrits en classe 3ème pendant les années scolaires 2017-2018 et 2018-2019
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
2

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.

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L'Apprentissage Par Projet (APP) est une méthode d'enseignement orientée apprenant, qui leur permet de réaliser des projets sous forme d'enquêtes approfondies. L'APP offre aux apprenants la possibilité de planifier leur projet, de collaborer avec leurs pairs et de rechercher les ressources pour atteindre les objectifs du projet. Cependant, l'APP est difficile à mettre en œuvre avec succès du fait que les apprenants manquent souvent des compétences d'autorégulation pour suivre, réfléchir, gérer et évaluer les activités durant le projet. L'apprentissage autorégulé peut aider les apprenants à acquérir ces compétences. Cependant, la plupart des environnements d'apprentissage utilisés en APP proposent surtout des matériaux d'apprentissage riches aux apprenants, et rarement les moyens de suivre et analyser leurs processus de gestion de projet et d'apprentissage. L'objectif principal de cette thèse est de soutenir l'apprentissage autorégulé en apprentisage par projet. Nous proposons une architecture générale de système de gestion des apprentissage par projet (PBLMS) qui aide les apprenants à comprendre comment réguler leurs activités d'apprentissage au cours d'un projet. Cette architecture générale intègre un système existant de gestion des apprentissages (LMS) et deux outils que nous proposons: un outil de reporting et un tableau de bord dynamique. L'outil de reporting supporte les processus de réflexion des apprenants en les amenant à décrire leurs activités non instrumentées, leurs réflexions et leurs évaluations sur les activités menées durant le projet à l'aide de phrases semi-structurées. Le système enregistre automatiquement les traces des interactions des utilisateurs avec le LMS, l'outil de reporting et le tableau de bord. Ces traces d'activité sont fusionnées avec les données autodéclarées afin que les indicateurs puissent être calculés sur la base de ces deux types d'informations. Le tableau de bord dynamique permet aux apprenants de créer des indicateurs personnalisables. Les apprenants peuvent spécifier les données à prendre en compte, le calcul et les modes de visualisation. Nous avons implémenté cette proposition théorique avec le développement de la plate-forme DDART (tableau de bord dynamique basé sur les traces d'activité et déclarées) qui intègre l'outil de reporting et le tableau de bord dynamique. Pour évaluer notre proposition, nous avons tout d'abord testé la capacité de DDART à créer un large échantillon d'indicateurs qui sont proposés dans les recherches existantes sur l'analyse des activités, la cognition, les émotions et les réseaux sociaux. De plus, une expérience a été menée afin d'évaluer l'utilisabilité et l'utilité perçue de DDART. Selon les résultats de cette expérience, nous avons constaté que DDART supporte les réflexions des apprenants sur la façon dont ils mènent leur projet et leur fournit les moyens de suivre leurs activités et apprentissages, même si la création d'indicateurs apparait difficile pour les novices
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

Книги з теми "Numeric traces and learning indicators":

1

Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research, eds. 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.

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2

Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research., eds. 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.

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3

Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research, eds. 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.

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Clifford, Adelman, ed. 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.

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Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research, eds. 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.

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Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research., eds. 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.

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7

Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research., eds. 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.

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Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research, eds. 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.

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9

Clifford, Adelman, and United States. Office of Educational Research and Improvement. Office of Research., eds. 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.

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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.

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Частини книг з теми "Numeric traces and learning indicators":

1

Chihab, Lamyaa, Abderrahim El Mhouti, and 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.

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2

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.

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This contribution attempts to introduce a systemic view of autonomy. The author argues that systemic autonomy depends on structures, procedures and regulations that are in force at organisations and that can either hinder or promote autonomy. The article reviews traces of systemic autonomy in the literature and develops a descriptive framework of the concept. It presents a scale containing nine indicators for measuring an institution’s degree of systemic autonomy, which teachers may easily apply to their own context. The article also includes recommendations for encouraging autonomy in restrictive environments. The author further compares his experiences from two different higher-education institutions: as a teacher at a university of applied sciences and as a learner at a university. The observations gained from this comparison lead to the conclusion that the influence of organisational systems cannot be ignored when expecting autonomy to unfold.
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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.

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Global social indicators, as a form of governance and soft regulation, can exert pressure for change and compliance through the way they compare and then rank the relative performance of states or other units. Is it reasonable to expect the comparisons they make in the process of carrying out such strategic exercises to be accurate and fair? In particular, how far can they, or should they, be required to be faithful to the requirement to “compare like with like”? This chapter first summarises some of the key features of global social indicators. It then goes on to analyse the differences (and overlap) between the tasks of comparing (learning about similarities and differences) and commensuration (showing equivalence and seeking to make matters come into line). Using as an example the role of indicators in documenting and responding to the current coronavirus epidemic, the chapter traces the way the hybrid and sometimes inconsistent commitment to both comparison and commensuration helps account for the difficulty they have had so far at establishing stable rankings of best practice. What can be learnt may also be of more general relevance.

Тези доповідей конференцій з теми "Numeric traces and learning indicators":

1

Tuns, Adrian-Ioan, and 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.

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Djouad, Tarek, Alain Mille, Christophe Reffay, and 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.

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Pantović, Danijela, and 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.

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The future of tourism is seen today as a key force in the process of globalization. When learning about culture, the origin of the destination is also very important, and the story of the destination is interesting if it has the history of a given culture. This paper generates short-term indicators of the development of the municipality of Vrnjačka Banja as a spa town in the Republic of Serbia with the largest market share. Apart from the fact that tourism is characterized by a highly global character and mass, it also creates knowledge about culture by connecting diferent parts of the world. The findings indicate that the existing tourist attractiveness does not exceed the potential possibilities, and in that sense it would be necessary to develop future tourist flows for the development and valorization of cultural heritage in Vrnjacka Banja. The results offer important implications for the implementation of tourism policy in the future, both for policy makers and for all stakeholders.

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