標(biāo)題: Titlebook: Assessment Analytics in Education; Designs, Methods and Muhittin Sahin,Dirk Ifenthaler Book 2024 The Editor(s) (if applicable) and The Auth [打印本頁(yè)] 作者: 評(píng)估 時(shí)間: 2025-3-21 16:37
書(shū)目名稱Assessment Analytics in Education影響因子(影響力)
書(shū)目名稱Assessment Analytics in Education影響因子(影響力)學(xué)科排名
書(shū)目名稱Assessment Analytics in Education網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Assessment Analytics in Education網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Assessment Analytics in Education被引頻次
書(shū)目名稱Assessment Analytics in Education被引頻次學(xué)科排名
書(shū)目名稱Assessment Analytics in Education年度引用
書(shū)目名稱Assessment Analytics in Education年度引用學(xué)科排名
書(shū)目名稱Assessment Analytics in Education讀者反饋
書(shū)目名稱Assessment Analytics in Education讀者反饋學(xué)科排名
作者: Instantaneous 時(shí)間: 2025-3-21 23:11 作者: analogous 時(shí)間: 2025-3-22 03:10 作者: 剛開(kāi)始 時(shí)間: 2025-3-22 06:00
Matias Sepulveda,Estefania Birrerearning whilst being independent of prior academic achievements. Concurrently, it has been shown that high-information feedback has the largest effect sizes for learning outcomes and academic performance. The following chapter provides insights to an approach to provide formative feedback supported 作者: 使成波狀 時(shí)間: 2025-3-22 12:05
Sang-Hun Lee MD,Hong-Geun Jung MD, PhD. The potency of learning analytics rests on both understanding relational dynamics and demonstrating their generalisability and temporal stability—aspects often underexplored in the literature. Recognising the benefits of formative assessments with corresponding feedback in promoting short- and lon作者: Acquired 時(shí)間: 2025-3-22 15:08
Jessica J. M. Telleria,Bruce Sangeorzann studied extensively, researchers have only recently begun to systematically examine student diversity in CSCL. Building on a theoretical integration of social psychology research with the CSCL literature, this chapter reports some key findings from an ongoing series of coordinated studies designed作者: 滋養(yǎng) 時(shí)間: 2025-3-22 20:34
Jessica J. M. Telleria,Bruce Sangeorzanributed standardized tests in Imperial China to current international tests such as the Programme for International Student Assessment (PISA) and the recent rise of Massive Open Online Courses (MOOCs), scale has motivated and dictated change in assessment. However, while the focus has been on the in作者: 牛馬之尿 時(shí)間: 2025-3-22 21:27
Minimally Invasive Ankle Arthrodesis tests are widely accepted for large-scale tests and classroom assessments, guiding learning and assessing learners’ progress is also needed. Advances in cognitive psychology have led to an in-depth understanding of how learners acquire and use knowledge. Researchers faced the challenges that determ作者: 政府 時(shí)間: 2025-3-23 02:35
Eva Umoh Asomugha,Adam T. Grothlgorithms that rely on few assumptions. Originally developed as a model for pairwise ratings of chess players, Elo is also used in educational contexts. In formative assessment environments where students can assess their own learning, students are challenged with assessment tasks. As a result of th作者: 補(bǔ)充 時(shí)間: 2025-3-23 07:00
First Metatarsophalangeal Joint Arthrodesislevel of any student more robustly than other measurement models. This strength of IRT is also important for its use in formative assessments. In the context of assessment analytics, learner ability estimated from IRT can be used in both test-based feedback (criterion-referenced, norm-referenced, an作者: Incorruptible 時(shí)間: 2025-3-23 10:52 作者: intricacy 時(shí)間: 2025-3-23 16:28 作者: atopic 時(shí)間: 2025-3-23 20:29
John S. Lewis Jr. MD,Mark E. Easley MDng the program’s aims is to determine the ablest interns and connect them with top employers in a merit-based system. Hence, all applying interns are put through an assessment procedure that determines their level of competence and capability. To be able to develop such a system, the program is desi作者: 懶惰人民 時(shí)間: 2025-3-24 00:10 作者: right-atrium 時(shí)間: 2025-3-24 02:24 作者: Clumsy 時(shí)間: 2025-3-24 09:16
https://doi.org/10.1007/978-3-030-58108-4 possible to design interactive digital stories and use them as a mean to deliver the educational content in an engaging and memorable manner. These allow the learners to control the outcome of the story based on their interactions. As a result, the learner receives a personalized experience, and it作者: 空洞 時(shí)間: 2025-3-24 14:37
https://doi.org/10.1007/978-3-319-69617-1such indicators. Successful development—that is, developing traceable, interpretable, and sensitive-to-learning indicators—requires understanding the underlying theory, how the theory is instantiated under different game conditions, and programming that considers these exigencies using fine-grained 作者: 忘川河 時(shí)間: 2025-3-24 15:11
https://doi.org/10.1007/978-3-031-56365-2Learning analytics; assessment analytics; feedback theory; intervention; feedback design; learning theori作者: CONE 時(shí)間: 2025-3-24 21:20 作者: 臥虎藏龍 時(shí)間: 2025-3-25 02:44 作者: 防銹 時(shí)間: 2025-3-25 04:26
Muhittin Sahin,Dirk IfenthalerProvides advances in assessment analytics.Introduces new collaborations‘on designs, methods and solutions in assessment.Offers practical solutions to researchers and practitioners作者: Physiatrist 時(shí)間: 2025-3-25 11:25
Advances in Analytics for Learning and Teachinghttp://image.papertrans.cn/b/image/163323.jpg作者: collagen 時(shí)間: 2025-3-25 12:08
Assessment Analytics in Education978-3-031-56365-2Series ISSN 2662-2122 Series E-ISSN 2662-2130 作者: 群居男女 時(shí)間: 2025-3-25 19:04
Foot and Ankle Biomechanics Gait Analysisions and feedback. AA specifically targets assessment systems, analyzing metrics like time spent on questions. The chapter explores AA’s definitions, frameworks, stakeholders, and research, emphasizing its importance in refining assessment processes and understanding learner experiences.作者: 西瓜 時(shí)間: 2025-3-25 20:05 作者: 刺耳 時(shí)間: 2025-3-26 03:05
2662-2122 utions to researchers and practitionersThis book is about the current state of research in online assessment.?The growth of this field is set to accelerate exponentially with emerging opportunities for automatic data collection and analysis. Yet, the future of online assessment faces major challenge作者: 切掉 時(shí)間: 2025-3-26 04:33
https://doi.org/10.1007/978-3-319-69617-1ased indicators, and games-specific indicators. For each example, the theoretical background is presented briefly, followed by a description of how the indicator design flows from the theory. Correlational analyses show how the GBIs relate to external criterion measures. Limitations and next steps are discussed.作者: 滑稽 時(shí)間: 2025-3-26 11:24
From Clicks to Constructs: An Examination of Validity Evidence of Game-Based Indicators Derived fromased indicators, and games-specific indicators. For each example, the theoretical background is presented briefly, followed by a description of how the indicator design flows from the theory. Correlational analyses show how the GBIs relate to external criterion measures. Limitations and next steps are discussed.作者: 缺乏 時(shí)間: 2025-3-26 15:09 作者: 一個(gè)姐姐 時(shí)間: 2025-3-26 18:47
Assessment Analytics for Digital Assessments Identifying, Modeling, and Interpreting Behavioral Engacusses the role of behavioral engagement in digital assessments, including computer-based tests and intelligent tutoring systems. We examine research on behavioral engagement and explain how it can be utilized in assessment analytics. As we describe state-of-the-art methods for modeling and interpre作者: HPA533 時(shí)間: 2025-3-26 21:38
Guiding Students Towards Successful Assessments Using Learning Analytics From Behavioral Data to Forentries can be used to calculate these measures? This contribution looks at indicators focusing on data for supporting metacognitive learning strategies and illustrates especially the process to extract measures of behavioral engagement from raw log data and its conversion into high-information feed作者: Flustered 時(shí)間: 2025-3-27 02:36 作者: 秘傳 時(shí)間: 2025-3-27 08:09 作者: 樹(shù)木中 時(shí)間: 2025-3-27 09:43
A Plurality of Measures: From Scale to Modality: Mapping Changes in Assessment and Its Implications a to the present day. The second part identifies two major hurdles for learning analytics to overcome, the development of clear methodology to interpret new assessments and the political process of having these interpretations accepted by stakeholders in the educational system. The chapter concludes作者: 母豬 時(shí)間: 2025-3-27 14:20 作者: slow-wave-sleep 時(shí)間: 2025-3-27 18:58 作者: 食物 時(shí)間: 2025-3-28 00:14
Student–Facing Assessment Analytics Dashboards Based on Rasch Measurement Theoryried out for this purpose. In the first two meso cycles of the design-based research process, a student-facing assessment analytics dashboard was designed and developed as well as a testing module. The reflection and evaluation phases of the study are ongoing. In this study, the learner dashboard is作者: Reclaim 時(shí)間: 2025-3-28 05:13
Analysis of Process Data to Advance Computer-Based Assessments in Multilingual Contextsdevelopment and administration process, hence, quality assurance. Based on the existing literature, we conceptualize a framework with five aspects of how process data can be used for assessment quality improvement. In addition, we set these aspects in the context of computer-based assessments in mul作者: 凝結(jié)劑 時(shí)間: 2025-3-28 07:03
Hierarchical Clustering in Profiling University Students for Online Teaching and Learningss. In a more realistic setting, the labels are not fully known until the semester end and supervised learning models may then create learning bias when trained only using assessment data. Therefore, cluster analysis presents a promising approach through observing performance similarities between st作者: 惰性氣體 時(shí)間: 2025-3-28 10:39
Managing a Large Talent Pool Using Assessment Analytics Within the Context of the National Internshi in experiential learning contexts. For these aims, the study focuses on the practices for the management of the talent pool of NIP to investigate how data-driven decision-making was supported and competence-based application evaluation was assured. These practices and the program overall were evalu作者: 他一致 時(shí)間: 2025-3-28 17:02 作者: Intractable 時(shí)間: 2025-3-28 19:24
An Architecture for Formative Assessment Analytics of Multimodal Artefacts in ePortfolios Supported rning modules. Our aim is to analyse the various modalities of produced multimodal content, such as ePortfolios, and to provide teachers with explainable metrics that represent human assessment rubrics in order to generate personalised feedback. To demonstrate the feasibility of the architecture, we作者: 幼兒 時(shí)間: 2025-3-29 00:36 作者: induct 時(shí)間: 2025-3-29 05:18
Book 2024ity of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings, further opportunities arise for advancing pedagogical assessment practice (Ifenthaler et al., 2018).?.This book fully details these opportunities, as 作者: 使腐爛 時(shí)間: 2025-3-29 10:53 作者: 易彎曲 時(shí)間: 2025-3-29 12:36
Carlos Pargas,Pablo Wagner Hitschfeldly, obtained clusters were named while considering the quiz-taking behaviors and academic achievements of the students within the clusters. The obtained clusters were named as follows: ., ., ., and .. It is expected that the findings obtained will contribute to the literature in understanding the dy作者: CAJ 時(shí)間: 2025-3-29 17:44 作者: Encapsulate 時(shí)間: 2025-3-29 22:45
Matias Sepulveda,Estefania Birrerentries can be used to calculate these measures? This contribution looks at indicators focusing on data for supporting metacognitive learning strategies and illustrates especially the process to extract measures of behavioral engagement from raw log data and its conversion into high-information feed作者: Generosity 時(shí)間: 2025-3-30 02:32
Sang-Hun Lee MD,Hong-Geun Jung MD, PhDe attitude and effort, which were consistent across the different courses. Given the identified relationships and their consistency, learning analytics has the potential to significantly enhance the quality of tertiary education. Further, these findings emphasise the critical importance of leveragin作者: connoisseur 時(shí)間: 2025-3-30 06:53
Jessica J. M. Telleria,Bruce Sangeorzanrdinated series of three empirical studies using social network analysis with digital behavioral data from 4628 distance learners in 930 groups. We use an unobtrusive assessment analytics approach to collect and analyze learner data from both formative and summative assessments, thereby mitigating t作者: 彩色的蠟筆 時(shí)間: 2025-3-30 09:18
Jessica J. M. Telleria,Bruce Sangeorzana to the present day. The second part identifies two major hurdles for learning analytics to overcome, the development of clear methodology to interpret new assessments and the political process of having these interpretations accepted by stakeholders in the educational system. The chapter concludes作者: xanthelasma 時(shí)間: 2025-3-30 15:27 作者: RENAL 時(shí)間: 2025-3-30 16:57
Eva Umoh Asomugha,Adam T. Grothels between students and assessment tasks, the dynamically calculated scores for both sides were discussed in the context of assessment analytics. Elo rating facilitates the simultaneous estimation of both learner skill and task difficulty, offering an approach for dynamically recalculating and upda作者: mendacity 時(shí)間: 2025-3-30 23:48
First Metatarsophalangeal Joint Arthrodesisried out for this purpose. In the first two meso cycles of the design-based research process, a student-facing assessment analytics dashboard was designed and developed as well as a testing module. The reflection and evaluation phases of the study are ongoing. In this study, the learner dashboard is作者: Increment 時(shí)間: 2025-3-31 01:47
https://doi.org/10.1007/978-3-030-62926-7development and administration process, hence, quality assurance. Based on the existing literature, we conceptualize a framework with five aspects of how process data can be used for assessment quality improvement. In addition, we set these aspects in the context of computer-based assessments in mul作者: Clinch 時(shí)間: 2025-3-31 06:37
Thomas J?llenbeck,Juliane Pietschmannss. In a more realistic setting, the labels are not fully known until the semester end and supervised learning models may then create learning bias when trained only using assessment data. Therefore, cluster analysis presents a promising approach through observing performance similarities between st
