Enhance student engagement, collaboration, and feedback in both asynchronous and synchronous learning with FeedbackFruits LMS-integrated teaching tools.
Seit dem 01. Dezember 2021 fördert das Bundesministerium für Bildung und Forschung (BMBF) im Rahmen der Förderlinie „Digitale Hochschulbildung“ das Verbundprojekt IMPACT – Implementierung von KI-basiertem Feedback und Assessment mit Trusted Learning Analytics in Hochschulen.
‘Automated Feedback’ has existed in various forms at UTS for quite some time: from online quizzes, to adaptive learning platforms, to conditional messaging. But…
The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs.
. The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs.
I chose this video because it highlights how technology can be used to differentiate instruction and of course assessment. I think this is one of the biggest areas where technology can be a game changer in terms of presenting material in different manners and allowing students to show their knowledge and application in different ways. The comments about day to day feedback and self assessment was a theme I found in several of the clips and articles.
This video looks at improving assessments so they don’t just measure learning but help create learning. It had some great examples of where folks go wrong and focuses on higher education where I preside. I enjoyed how it outlined better steps to lead to learning. Thinking about incorporating peer feedback as well as the need for good rubrics played in well to the greater themes.
AQUA - Automatic Quality Assessment and Feedback in eLearning 2.0
The current development of Web 2.0 makes the distinction between author and reader fading away. Users now produce huge amounts of data which sometimes is of questionable quality. This leads to the problem of information overload: how to make the most of this information without overwhelming the users? One key challenge to solve this issue is to assess the quality of the user generated content.
In AQUA, we seek to develop algorithms to assess the quality of content automatically. We focus on two sources for this assessment: (1) user generated content; (2) feedback by users of the content. To do so, we investigate techniques from the fields of natural language processing (NLP), information retrieval, and machine learning.
So, in a nutshell, AQUA will answer the following questions:
What is quality of information? How does it matter in information search?
How to model the quality of user generated content?
How far can you go with automatic methods in assessing quality?
How to give feedback to users regarding quality?
The AQUA project is associated with the project "Mining Lexical-Semantic Knowledge from Dynamic and Linguistic Sources and Integration into Question Answering for Discourse-Based Knowledge Acquisition in e-learning (QA-EL)".
The Student Enhanced Learning through Effective Formative Feedback (SENLEF) project team have produced a publication with resources for practitioners wishing to improve their feedback practice to students or get some new ideas on how to enhance their current practice.
Over a period of nine months examples of current practice and innovative ideas on providing formative assessment feedback to students were collected from Scottish higher education institutions. The information gathered provided a collection of case studies, which forms the basis of the project.
This project also brings together the research on formative assessment and feedback and provides principles for good practice and workshop materials that will be included in the publication.
The publication includes: -
- A series of case studies
- A theoretical model
- Seven principles for good effective practice
- Workshop plans for using the materials.
S. Ambrose, M. Bridges, M. DiPietro, M. Lovett, and M. Norman. John Wiley & Sons, (2010)Learning results from what the student does and thinks and only from what the student does and thinks. The teacher can advance learning only by influencing what the student does to learn. Herbert Simon.
K. Wang, B. Lin, B. Rettig, P. Pardi, and R. Singh. Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, page 257--260. New York, NY, USA, ACM, (2017)
K. Wang, B. Lin, B. Rettig, P. Pardi, and R. Singh. Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, page 257--260. New York, NY, USA, ACM, (2017)
N. Herbert. ACE '07: Proceedings of the ninth Australasian conference on Computing education, page 63--71. Darlinghurst, Australia, Australia, Australian Computer Society, Inc., (2007)