標(biāo)題: Titlebook: Artificial Intelligence in Education; 22nd International C Ido Roll,Danielle McNamara,Vania Dimitrova Conference proceedings 2021 Springer [打印本頁(yè)] 作者: 回憶錄 時(shí)間: 2025-3-21 16:29
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書(shū)目名稱(chēng)Artificial Intelligence in Education被引頻次
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書(shū)目名稱(chēng)Artificial Intelligence in Education讀者反饋
書(shū)目名稱(chēng)Artificial Intelligence in Education讀者反饋學(xué)科排名
作者: 變異 時(shí)間: 2025-3-21 20:37
The Mafia Family: Organised Crime Families,s. Our results, conducted over 35 067 students and evaluated over 32,538 students, show that existing prediction models do indeed seem to favour the majority group. As opposed to hypothesise, creating individual models does not help improving accuracy or fairness.作者: 樹(shù)上結(jié)蜜糖 時(shí)間: 2025-3-22 04:20
Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally?s. Our results, conducted over 35 067 students and evaluated over 32,538 students, show that existing prediction models do indeed seem to favour the majority group. As opposed to hypothesise, creating individual models does not help improving accuracy or fairness.作者: Daily-Value 時(shí)間: 2025-3-22 08:13
Open Learner Models for Multi-activity Educational Systemsge state based on their engagement with multiple types of learning activities. We apply MA-Elo to three data sets obtained from an educational system supporting multiple student activities. Results indicate that the proposed approach can provide a higher predictive performance compared with baseline and some state-of-the-art learner models.作者: 四海為家的人 時(shí)間: 2025-3-22 12:25 作者: Ondines-curse 時(shí)間: 2025-3-22 15:51
Agent-Based Classroom Environment Simulation: The Effect of Disruptive Schoolchildren’s Behaviour Ver parameters of peers and teacher’s characteristics, which we believe renders a more realistic setting. Specifically, we explore the effect of . and .. The dataset used for the design of our model consists of 65,385 records, which represent 3,315 classes in 2007, from 2,040 schools in the UK.作者: fructose 時(shí)間: 2025-3-22 20:38 作者: 別名 時(shí)間: 2025-3-23 00:53
National Symposium on Family Issuesge state based on their engagement with multiple types of learning activities. We apply MA-Elo to three data sets obtained from an educational system supporting multiple student activities. Results indicate that the proposed approach can provide a higher predictive performance compared with baseline and some state-of-the-art learner models.作者: 挑剔為人 時(shí)間: 2025-3-23 04:57
https://doi.org/10.1057/978-1-137-59028-2tify motivational factors related to students’ collaborative behaviors; and develop a set of representative personas. These personas could be embedded in an interface and be used as an alternative method to assess motivation within ITS.作者: 粘土 時(shí)間: 2025-3-23 09:37
Matteo Moscatelli,Donatella Bramantir parameters of peers and teacher’s characteristics, which we believe renders a more realistic setting. Specifically, we explore the effect of . and .. The dataset used for the design of our model consists of 65,385 records, which represent 3,315 classes in 2007, from 2,040 schools in the UK.作者: 并置 時(shí)間: 2025-3-23 11:13
0302-9743 e in Education, AIED 2021, held in Utrecht, The Netherlands, in June 2021.*.The 40 full papers presented together with 76 short papers, 2 panels papers, 4 industry papers, 4 doctoral consortium, and 6 workshop papers were carefully reviewed and selected from 209 submissions. The conference provides 作者: Geyser 時(shí)間: 2025-3-23 16:58
Matteo Moscatelli,Donatella Bramanti the proposed framework uses IRT to average prediction scores from various AES models while considering the characteristics of each model for evaluation of examinee ability. This study demonstrates that the proposed framework provides higher accuracy than individual AES models and simple averaging methods.作者: aspersion 時(shí)間: 2025-3-23 19:16
Detlev Lück,Eric D. Widmer,Vida ?esnuityt?iveness and efficiency of these systems. This challenge has been cited by a number of researchers as one of the most important for the field of AIED. In this paper, we discuss existing progress towards resolving this challenge, break down five sub-challenges, and propose how to address the sub-challenges.作者: 配偶 時(shí)間: 2025-3-24 00:20
https://doi.org/10.1007/978-3-030-71169-6bserved systematicity in machine error, namely, that cases with low estimated reading accuracy are harder to score correctly for fluency. We show that the method yields an improved performance, including on out-of-domain data.作者: Noctambulant 時(shí)間: 2025-3-24 04:56
Integration of Automated Essay Scoring Models Using Item Response Theory the proposed framework uses IRT to average prediction scores from various AES models while considering the characteristics of each model for evaluation of examinee ability. This study demonstrates that the proposed framework provides higher accuracy than individual AES models and simple averaging methods.作者: 合法 時(shí)間: 2025-3-24 08:06 作者: Badger 時(shí)間: 2025-3-24 14:15
Exploiting Structured Error to Improve Automated Scoring of Oral Reading Fluencybserved systematicity in machine error, namely, that cases with low estimated reading accuracy are harder to score correctly for fluency. We show that the method yields an improved performance, including on out-of-domain data.作者: BAIL 時(shí)間: 2025-3-24 18:22
Diane S. Lauderdale,Jen-Hao Chenrovided vocabulary lists, we compare them to the vocabulary needed by 37 Syrian refugees living in Lebanon and Germany. We show that the vocabulary provided by the Cambridge English List and Duolingo has low usefulness and low efficiency and discuss future directions for personal vocabulary recommendations.作者: ALIAS 時(shí)間: 2025-3-24 20:38
Diane S. Lauderdale,Jen-Hao Chenaper, we introduce Social Coherence (SC), another marker of collaboration, and our analysis shows that WC-GCMS is sensitive to the SC level of group discourse, further validating the potency of the metric.作者: pus840 時(shí)間: 2025-3-25 01:09 作者: PHONE 時(shí)間: 2025-3-25 05:51
Vida ?esnuityt?,Detlev Lück,Eric D. Widmerere representative of student performance from the training dataset, while the GAN trained on all features was not able to capture characteristics from the dataset. Based on the results, the synthetic dataset can provide an alternative unrestricted source of data without compromising student privacy.作者: Tailor 時(shí)間: 2025-3-25 07:37 作者: 衰老 時(shí)間: 2025-3-25 13:59 作者: critique 時(shí)間: 2025-3-25 16:43 作者: Flawless 時(shí)間: 2025-3-25 20:05 作者: BLAZE 時(shí)間: 2025-3-26 02:04
Generation of Automatic Data-Driven Feedback to Students Using Explainable Machine Learningautomatically provides data-driven recommendations for action. The underlying predictive model effectiveness of the proposed approach is evaluated, with the results demonstrating 90 per cent accuracy.作者: 物種起源 時(shí)間: 2025-3-26 07:58
Protecting Student Privacy with?Synthetic Data from Generative Adversarial Networksere representative of student performance from the training dataset, while the GAN trained on all features was not able to capture characteristics from the dataset. Based on the results, the synthetic dataset can provide an alternative unrestricted source of data without compromising student privacy.作者: Venules 時(shí)間: 2025-3-26 11:17 作者: VALID 時(shí)間: 2025-3-26 14:20 作者: itinerary 時(shí)間: 2025-3-26 20:48
Conference proceedings 2021tion, AIED 2021, held in Utrecht, The Netherlands, in June 2021.*.The 40 full papers presented together with 76 short papers, 2 panels papers, 4 industry papers, 4 doctoral consortium, and 6 workshop papers were carefully reviewed and selected from 209 submissions. The conference provides opportunit作者: THROB 時(shí)間: 2025-3-26 22:46
Scrutability, Control and Learner Models: Foundations for Learner-Centred Design in AIED ways that personal data is harvested and used. This makes it timely to draw on the decades of AIED research towards creating systems and interfaces that enable learners to truly harness and control their learning data. This invited keynote will present a whirlwind tour of my learner modelling resea作者: 青石板 時(shí)間: 2025-3-27 04:10 作者: 舔食 時(shí)間: 2025-3-27 09:18
Personal Vocabulary Recommendation to Support Real Life Needss. Immigrants, refugees, students abroad learn a language to navigate through their daily lives and often need words that are missing from their curricula they study. Today’s language learners rely heavily on digital translators and dictionaries, creating a database of words they need in their every作者: 愚蠢人 時(shí)間: 2025-3-27 09:58
Artificial Intelligence Ethics Guidelines for K-12 Education: A Review of the Global Landscapecompared and contrasted concerns raised and principles applied. We found that while AIEdK-12 ethics guidelines employed many principles common to non-AIEd policy statements (e.g., transparency), new ethical principles were being engaged including pedagogical appropriateness and children’s rights.作者: 憤怒歷史 時(shí)間: 2025-3-27 17:25 作者: 一起 時(shí)間: 2025-3-27 18:49
Generation of Automatic Data-Driven Feedback to Students Using Explainable Machine Learningtelligent actionable feedback that supports students self-regulation of learning in a data-driven manner. Prior studies within the field of learning analytics predict students’ performance and use the prediction status as feedback without explaining the reasons behind the prediction. Our proposed me作者: 催眠 時(shí)間: 2025-3-27 23:36 作者: 毀壞 時(shí)間: 2025-3-28 02:25 作者: nautical 時(shí)間: 2025-3-28 09:34
Integration of Automated Essay Scoring Models Using Item Response Theoryproposed over the past few decades. This study proposes a new framework for integrating AES models that uses item response theory (IRT). Specifically, the proposed framework uses IRT to average prediction scores from various AES models while considering the characteristics of each model for evaluati作者: Texture 時(shí)間: 2025-3-28 12:51
Towards Sharing Student Models Across Learning Systems behaviors, and affect—is not carried over to other systems that could benefit students by using the information, potentially reducing both the effectiveness and efficiency of these systems. This challenge has been cited by a number of researchers as one of the most important for the field of AIED. 作者: 河流 時(shí)間: 2025-3-28 14:41 作者: 吹牛大王 時(shí)間: 2025-3-28 21:54
Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally?ity Ethnic (BAME) students, with similar effects found when comparing students across other protected attributes, such as gender or disability. In this paper, we study whether existing prediction models to identify students at risk of failing (and hence providing early and adequate support to studen作者: sebaceous-gland 時(shí)間: 2025-3-29 02:26 作者: FUSC 時(shí)間: 2025-3-29 05:50 作者: 改變 時(shí)間: 2025-3-29 10:38
The School Path Guide: A Practical Introduction to Representation and Reasoning in AI for High Schoowhich are completely new for them at this educational level. The activity has been designed in the scope of the Erasmus+?project called AI+, which aims to develop a curriculum of Artificial Intelligence (AI) for high school students in Europe. As established in the AI+?principles, all the teaching a作者: muffler 時(shí)間: 2025-3-29 14:46
Kwame: A Bilingual AI Teaching Assistant for Online SuaCode CoursesOnline environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of SuaCode students—learners across 42 African countries that are mostly Anglophone or Francophone—in this work, we developed a bilingual Artificial Intellig作者: Redundant 時(shí)間: 2025-3-29 17:28
Early Prediction of Children’s Disengagement in a Tablet Tutor Using Visual Featuresarly warning signs of disengagement in time to prevent it. Toward that goal, this paper describes a method that uses input from a tablet tutor’s user-facing camera to predict whether the student will complete the current activity or disengage from it. Training a disengagement predictor is useful not作者: Immortal 時(shí)間: 2025-3-29 20:49 作者: defendant 時(shí)間: 2025-3-30 02:53
978-3-030-78269-6Springer Nature Switzerland AG 2021作者: Mediocre 時(shí)間: 2025-3-30 07:05
Artificial Intelligence in Education978-3-030-78270-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 影響 時(shí)間: 2025-3-30 10:41 作者: obeisance 時(shí)間: 2025-3-30 13:39 作者: GAVEL 時(shí)間: 2025-3-30 17:29
Family Caregiving in Aging Populations ways that personal data is harvested and used. This makes it timely to draw on the decades of AIED research towards creating systems and interfaces that enable learners to truly harness and control their learning data. This invited keynote will present a whirlwind tour of my learner modelling resea作者: 泛濫 時(shí)間: 2025-3-31 00:38
National Symposium on Family Issuess higher-order learning activities such as creating resources, creating solutions, rating the quality of resources, and giving feedback. In response to this trend, this paper proposes an interpretable and open learner model called MA-Elo that capture an abstract representation of a student’s knowled作者: BRIBE 時(shí)間: 2025-3-31 04:46 作者: 美麗的寫(xiě) 時(shí)間: 2025-3-31 08:41
Diane S. Lauderdale,Jen-Hao Chencompared and contrasted concerns raised and principles applied. We found that while AIEdK-12 ethics guidelines employed many principles common to non-AIEd policy statements (e.g., transparency), new ethical principles were being engaged including pedagogical appropriateness and children’s rights.
