標(biāo)題: Titlebook: Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme; With Examples in R a Alina A. von Dav [打印本頁(yè)] 作者: Monomania 時(shí)間: 2025-3-21 17:42
書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme影響因子(影響力)
書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme影響因子(影響力)學(xué)科排名
書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme網(wǎng)絡(luò)公開(kāi)度
書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme被引頻次
書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme被引頻次學(xué)科排名
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書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme讀者反饋
書目名稱Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme讀者反饋學(xué)科排名
作者: Longitude 時(shí)間: 2025-3-21 21:00 作者: 揉雜 時(shí)間: 2025-3-22 03:55
2367-170X s hard-won psychometric insights to a larger universe of con.This book defines and describes a new discipline, named “computational psychometrics,” from the perspective of new methodologies for handling complex data from digital learning and assessment. The editors and the contributing authors discu作者: Enrage 時(shí)間: 2025-3-22 06:59
Supply-Strategien in Einkauf und Beschaffungm virtual learning and assessment systems. We also discuss here the structure of the edited volume, how each chapter contributes to enhancing the psychometrics science and our recommendations for further readings.作者: 改正 時(shí)間: 2025-3-22 09:46
https://doi.org/10.1007/978-94-009-5141-9discussed, as student knowledge is the most common learner characteristic widely assessed in large-scale adaptive systems. This chapter concludes with a discussion of the limitations of the current generation of adaptive learning systems, and areas of potential for future progress.作者: 蛛絲 時(shí)間: 2025-3-22 16:38 作者: 蛛絲 時(shí)間: 2025-3-22 19:58
Introduction to Computational Psychometrics: Towards a Principled Integration of Data Science and Mm virtual learning and assessment systems. We also discuss here the structure of the edited volume, how each chapter contributes to enhancing the psychometrics science and our recommendations for further readings.作者: Migratory 時(shí)間: 2025-3-22 22:34
Knowledge Inference Models Used in Adaptive Learningdiscussed, as student knowledge is the most common learner characteristic widely assessed in large-scale adaptive systems. This chapter concludes with a discussion of the limitations of the current generation of adaptive learning systems, and areas of potential for future progress.作者: 屈尊 時(shí)間: 2025-3-23 04:44
Text Mining and Automated Scoringis chapter, we aim at introducing some basics of NLP through two typical applications in educational contexts, text mining and automated scoring. We hope readers can get an overall picture of NLP and get familiarized with some basic tools for handling natural language data, which may serve as stepping stones for their future work with NLP.作者: 輕快來(lái)事 時(shí)間: 2025-3-23 07:20
Supply-Strategien in Einkauf und Beschaffung models, the Dynamic Bayesian Models that encompass many traditional psychometric models and machine-learning algorithms. We conclude by emphasizing that model complexity and power need to be balanced with the responsibility for transparency and fairness towards stakeholders.作者: Ingrained 時(shí)間: 2025-3-23 12:36
Support Networks in a Caring Communityhe data challenges from digitally based assessments. We describe some specific datatechniques to parse and process complex process data with example codes in Python programming language. We also outline the general methodological strategies when dealing with process data from digitally based assessments.作者: 友好關(guān)系 時(shí)間: 2025-3-23 15:51 作者: 鞏固 時(shí)間: 2025-3-23 21:51
A Data Science Perspective on Computational Psychometricshe data challenges from digitally based assessments. We describe some specific datatechniques to parse and process complex process data with example codes in Python programming language. We also outline the general methodological strategies when dealing with process data from digitally based assessments.作者: Adornment 時(shí)間: 2025-3-23 22:53 作者: Anthem 時(shí)間: 2025-3-24 03:09 作者: 笨拙的我 時(shí)間: 2025-3-24 09:30
Unsupervised Machine Learningaling; and five Python programming examples that demonstrate the learning concepts and results using psychometric assessment data collected from an online collaborative problem-solving environment. This chapter demonstrates the potential of machine learning and highlights the opportunities it presents in psychometric research and development.作者: Derogate 時(shí)間: 2025-3-24 13:53 作者: keloid 時(shí)間: 2025-3-24 15:16
Book 2021 data from digital learning and assessment. The editors and the contributing authors discuss how new technology drastically increases the possibilities for the design and administration of learning and assessment systems, and how doing so significantly increases the variety, velocity, and volume of 作者: accessory 時(shí)間: 2025-3-24 20:02 作者: 輕信 時(shí)間: 2025-3-25 00:20
Next Generation Learning and Assessment: What, Why and Howay aid the analyses of complex data from performance assessments. This chapter discusses the grounds for using complex performance assessments, the design of such assessments so that useful evidence about targeted abilities will be present in the data to be analysed, and roles that computational psy作者: 客觀 時(shí)間: 2025-3-25 05:30
Computational Psychometrics: A Framework for Estimating Learners’ Knowledge, Skills and Abilities frional psychometrics as a framework for the measurement of learners’ skills, knowledge, and abilities. We discuss the changes in educational measurement that led to the need for expanding the psychometrics toolbox and describe the properties of psychometric data. We then give an example of a class of作者: 苦笑 時(shí)間: 2025-3-25 07:36
Virtual Performance-Based Assessmentsin order to provide evidence about their knowledge, skills, or other attributes. Examples include tasks based on interactive simulations, games, branching scenarios, and collaboration among students communicating through digital chats. They may be used for summative purposes, as in certification exa作者: Fillet,Filet 時(shí)間: 2025-3-25 14:54
Knowledge Inference Models Used in Adaptive Learninge recent approaches to student modeling that infer student knowledge (i.e. what students know at any given moment during the learning experience) are discussed, as student knowledge is the most common learner characteristic widely assessed in large-scale adaptive systems. This chapter concludes with作者: SSRIS 時(shí)間: 2025-3-25 16:31
Concepts and Models from Psychometricsrediction and selection. Ideas emerged over that nevertheless hold value for the new psychological perspectives, contexts of use, and forms of data and analytic tools we are now seeing. In this chapter we review some fundamental models and ideas from psychometrics that are be profitably reconceived,作者: JOT 時(shí)間: 2025-3-25 21:55 作者: 猛擊 時(shí)間: 2025-3-26 00:25 作者: DOSE 時(shí)間: 2025-3-26 05:59
Supervised Machine Learningapter, we focus on an important branch of machine learning, supervised machine learning, and introduce three widely used supervised learning methods, the Support Vector Machine, Random forest, and Gradient Boosting Machine. Python codes examples are included to show how to use these methods in pract作者: Osteons 時(shí)間: 2025-3-26 09:29
Unsupervised Machine Learningth a brief background on machine learning and then a high-level discussion on the differences between supervised and unsupervised learning algorithms. We present three categories of unsupervised machine learning techniques that include clustering, outlier detection, and dimension reduction; five pre作者: 音樂(lè)會(huì) 時(shí)間: 2025-3-26 13:29
Advances in AI and Machine Learning for Education Researchs problem solving, communication and collaboration. In these real-world applications student data is captured with a high degree of granularity, variety of temporal scales and in a multitude of modalities. Unfortunately such complex, noisy and unstructured data limit the applicability of traditional作者: hysterectomy 時(shí)間: 2025-3-26 19:14 作者: Arthr- 時(shí)間: 2025-3-26 21:21 作者: browbeat 時(shí)間: 2025-3-27 02:16
Text Mining and Automated Scoring include automated scoring, automated item generation, conversation-based assessments, writing assistants, text mining for education, and so on. In this chapter, we aim at introducing some basics of NLP through two typical applications in educational contexts, text mining and automated scoring. We h作者: Ornament 時(shí)間: 2025-3-27 07:06
Supply-Strategien in Einkauf und Beschaffungof psychometric toolbox to include methodologies from machine learning and data science in order to address the complexities of big data collected from virtual learning and assessment systems. We also discuss here the structure of the edited volume, how each chapter contributes to enhancing the psyc作者: curriculum 時(shí)間: 2025-3-27 10:53 作者: Admire 時(shí)間: 2025-3-27 15:39 作者: Jejune 時(shí)間: 2025-3-27 21:39
Supply-Strategien in Einkauf und Beschaffungin order to provide evidence about their knowledge, skills, or other attributes. Examples include tasks based on interactive simulations, games, branching scenarios, and collaboration among students communicating through digital chats. They may be used for summative purposes, as in certification exa作者: APO 時(shí)間: 2025-3-27 22:03 作者: NEEDY 時(shí)間: 2025-3-28 05:46 作者: 檔案 時(shí)間: 2025-3-28 07:43 作者: Pelago 時(shí)間: 2025-3-28 11:00 作者: 功多汁水 時(shí)間: 2025-3-28 16:09
Support Networks in a Caring Communityapter, we focus on an important branch of machine learning, supervised machine learning, and introduce three widely used supervised learning methods, the Support Vector Machine, Random forest, and Gradient Boosting Machine. Python codes examples are included to show how to use these methods in pract作者: 刺耳 時(shí)間: 2025-3-28 20:13 作者: Alveoli 時(shí)間: 2025-3-29 02:00 作者: 現(xiàn)實(shí) 時(shí)間: 2025-3-29 03:28 作者: 行為 時(shí)間: 2025-3-29 09:28
Basic Statistical Analysis of SVMs,urrent and future assessment. In: Lissitz R (eds) Computers and their impact on state assessment: recent history and predictions for the future. Information Age Publishing, Charlotte, pp 273–306, 2012; Mislevy RJ, Oranje A, Bauer MI, Von Davier A, Hao J, Corrigan S, . . . John M, Psychometric consid作者: keloid 時(shí)間: 2025-3-29 14:08 作者: puzzle 時(shí)間: 2025-3-29 18:52 作者: overbearing 時(shí)間: 2025-3-29 19:48
Supervised Machine Learningapter, we focus on an important branch of machine learning, supervised machine learning, and introduce three widely used supervised learning methods, the Support Vector Machine, Random forest, and Gradient Boosting Machine. Python codes examples are included to show how to use these methods in practice.作者: Hemiplegia 時(shí)間: 2025-3-30 01:38 作者: 水汽 時(shí)間: 2025-3-30 05:05
Methodology of Educational Measurement and Assessmenthttp://image.papertrans.cn/c/image/232928.jpg作者: 合并 時(shí)間: 2025-3-30 10:11 作者: 分開(kāi) 時(shí)間: 2025-3-30 12:47 作者: 出價(jià) 時(shí)間: 2025-3-30 18:45 作者: BROTH 時(shí)間: 2025-3-30 20:41
The Social Services as “Network Organizers”polis-Hastings and the Gibbs sampler. A Metropolis-Hastings algorithm developed by Marsman et al. (Sci Rep 5:9050, 1–7, 2015) will be used to illustrate how MCMC can be done for a wide range of models in computational statistics.作者: Alopecia-Areata 時(shí)間: 2025-3-31 01:59
Support Networks in a Caring Communityapter, we focus on an important branch of machine learning, supervised machine learning, and introduce three widely used supervised learning methods, the Support Vector Machine, Random forest, and Gradient Boosting Machine. Python codes examples are included to show how to use these methods in practice.
