Titlebook: Artificial Intelligence in Education; 22nd International C Ido Roll,Danielle McNamara,Vania Dimitrova Conference proceedings 2021 Springer

THROB

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

青石板

舔食

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

愚蠢人

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.

憤怒歷史

一起

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

催眠

毀壞

nautical

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

Texture

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.