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Recursive Feedback - Evaluative Dimensions of Learning
Recursive Feedback—or a new generation of assessment systems, including continuous machine-mediated human assessment from multiple perspectives (peers, self, teacher, parents, invited experts etc.), and machine feedback (selected and supply response assessments, natural language processing). Student work can also be assessed through data mining techniques, analyzable either as individual progress, or comparisons across cohorts. Student are also offered just in time feedback, or assessment that is for learning (formative assessment) and not just of learning (summative assessment).
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Comment: Make a comment below this update about the ways in which recursive feedback technologies can change the nature of learning. Respond to others' comments with @name.
Post an Update: Make an update introducing a recursive feedback concept on the community page. Define the concept and provide at least one example of the concept in practice. Be sure to add links or other references, and images or other media to illustrate your point. If possible, select a concept that nobody has addressed yet so we get a well-balanced view of recursive feedback. Also, comment on at least three or four updates by other participants. Recursive feedback concepts might include:
- Formative assessment
- Continuous assessment
- Criterion-referenced (versus norm-referenced) assessment.
- Intelligent tutors
- Educational data mining
- Learning analytics
- Dashboards and mashups
- Quizzes
- Computer adaptive testing
- Diagnostic testing
- Peer review
- Automated writing evaluations
- Suggest a concept in need of definition!
Unveiling the Potential of Learning Analytics in Education: Insights, Impact, and Challenges
Introduction
In the ever-evolving landscape of education, the quest to optimize learning experiences has led to the emergence of a powerful tool: learning analytics. As technology intertwines with pedagogy, the collection, analysis, and interpretation of vast amounts of data have become fundamental in understanding how learners engage with educational content and environments.
Learning analytics stands at the nexus of educational data mining, machine learning, and pedagogical insight, offering a systematic approach to unravelling the complexities of student learning. It encompasses the systematic measurement, collection, analysis, and reporting of data, enabling educators and institutions to derive actionable insights that transform the educational landscape.
Educational institutions, armed with vast amounts of data derived from learning management systems, adaptive learning software, and predictive analytics, now have the potential to personalize learning experiences, predict student outcomes, and refine teaching methodologies. This transformative potential, however, is coupled with challenges concerning privacy, ethical use of data, and accurate interpretation.
Definition of Learning Analytics in Education
Learning analytics refers to the measurement, collection, analysis, and reporting of data about learners and their interactions with educational content and environments. It involves the use of various tools and techniques to derive meaningful insights that inform educational decision-making.
Examples of Learning Analytics
Learning Management Systems (LMS): Platforms like Canvas or Moodle collect data on student engagement, progress, and performance (Siemens & Long, 2011).
Adaptive Learning Software: Programs that adjust content based on individual student responses and performance, such as Khan Academy or Duolingo (Various sources from educational technology companies).
Predictive Analytics:
Analyzing historical data to forecast future student performance or identify students at risk of falling behind (Ifenthaler & Schumacher, 2016).
Impact of Learning Analytics
• Personalized Learning: Tailoring educational experiences to individual needs and learning styles (Ferguson, 2012).
• Improved Student Outcomes: Enhancing learning strategies and interventions to boost student success rates (Siemens, 2013).
• Data-informed decision-making: Educators and institutions can make informed decisions to optimize curriculum and teaching methodologies (Various sources from EDUCAUSE and SoLAR).
Uses of Learning Analytics
• https://edtechmagazine.com/higher/article/2023/06/learning-analytics-impact-higher-education-perfcon
• Identifying Learning Patterns: Recognizing trends and patterns in student behaviour to adapt teaching methods accordingly (Siemens & Long, 2011).
https://files.eric.ed.gov/fulltext/EJ1308353.pdf
• Early Intervention: Spotting struggling students early and providing timely interventions to support them (Ifenthaler & Schumacher, 2016).
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793614/
• Resource Allocation: Optimizing resource allocation based on identified needs and student performance (Various sources from educational technology platforms).
https://tech.ed.gov/files/2017/01/NETP17.pdf
Challenges in Learning Analytics
• Privacy Concerns: Balancing the collection of sensitive student data with ensuring privacy and security (Ferguson, 2012).
• Interpreting Data Correctly: Ensuring accurate analysis and interpretation of data to derive meaningful insights (Siemens, 2013).
• Ethical Use of Data: Upholding ethical standards in data usage, particularly when making decisions that affect students (Various sources from EDUCAUSE and SoLAR).
• Relevant Sub-Topics in Today's Educational System
• Remote Learning: How learning analytics can support and enhance remote or hybrid learning environments (Various sources from educational technology companies).
• Equity and Inclusion: Using data to address disparities in educational opportunities and outcomes among different student groups (Ferguson, 2012; Siemens & Long, 2011).
• Emerging Technologies: Integrating AI, machine learning, and other technologies in learning analytics to further improve educational experiences (Ifenthaler & Schumacher, 2016).
Conclusion
Learning analytics has tremendous potential to revolutionize education by providing valuable insights into student learning patterns and needs. However, while its benefits are substantial, careful consideration of ethical, privacy, and interpretive challenges is crucial to ensure its responsible and effective implementation.
These referenced resources offer scholarly insights, case studies, and theoretical frameworks on learning analytics in education, supporting the information provided earlier. Please ensure to cite these references properly according to your required citation style when using this information in your work.
References
• https://tech.ed.gov/files/2017/01/NETP17.pdf
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793614/
• https://files.eric.ed.gov/fulltext/EJ1308353.pdf
• https://edtechmagazine.com/higher/article/2023/06/learning-analytics-impact-higher-education-perfcon
Yes feedback feedback, feedback is very important.
التغذية الراجعة المستمرة في جميع مراجل التعلم تحفظ توجه التعلم للأمام
التغذية الراجعة المستمرة في جميع مراجل التعلم تحفظ توجه التعلم للأمام
التغذية الراجعة المستمرة في جميع مراجل التعلم تحفظ توجه التعلم للأمام
التغذية الراجعة المستمرة في جميع مراجل التعلم تحفظ توجه التعلم للأمام
التقويم من أجل التعلم
ينعكس اختلاف النظريات التعليمية على المدى الذي يتم تقييمه والطرق التي يتم استخدامها للتقويم، وهذا يؤثر بدوره على طريقة تصميم وتنفيذ الأنشطة التعليمية والتقويمية. وبالتالي، فإن فهم النظريات التعليمية وتأثيراتها على التقويم يمكن أن يساعدني على اتخاذ القرارات الأفضل لتحسين جودة التعليم والتقويم.
يمكن استخدام المشاريع والأنشطة العملية كأساليب تقويمية لتحديد مدى قدرة الطلاب على تطبيق المعرفة بشكل عملي. ويمكن تصميم المشاريع بحيث تتطلب تطبيق المعرفة والمهارات العملية والتفاعل الاجتماعي. ويتم تحديد الأساليب التقويمية المناسبة والملائمة وفقًا للنظرية التعليمية المستخدمة والأهداف المرجوة من النشاط التعليمي.
ويلحظ من مما سبق أن اختلاف الأساليب باختلاف نظريات التعلم بناء على عدة عوامل وهي النظريات نفسها، ولارتباط مكونات المنهج بعضها لبعض من الأهداف، والمعلم والطالب، والمادة التعليمية. و في تطبيق طريقة التعلم القائم على الاستقصاء، بحسب نظريات التعلم هناك من يرى أمثال أوزبل وسكنر أنها تستغرق وقت أطول مع العلم كل منها يتبنى نظرية تعلم تختلف عن الاخر، ويرى جانييه أنها طريقة تحتاج لتخطيط من المعلم والطلاب لديهم قدرة على حل المشكلات. ويستخدم أسلوب الملاحظة وسلم التقدير.
يتضح في النهاية أهمية التقويم من أجل التعلم في تحسين التعلم لدى المتعلمين ويجعلهم فاعلين، ويمارسونه في تقويم ذاتهم، ومع زملاءهم، ويعطي تصور للمعلمين عن تعلم المتعلمين وتقدمهم، وتقديم التغذية الراجعة بشكل دائم لأجل تحسين تعلمهم.
Formative Assessment is an ongoing intellectual analysis of the learning progress of the learners. The data gathered from this type of assessment helps ensure that there is learning that is taking place.
Automated writing evaluators are key in making an immediate feedback on the writing outputs of the learners.
Formative Assessment is an ongoing intellectual analysis of the learning progress of the learners. The data gathered from this type of assessment helps ensure that there is learning that is taking place.
Automated writing evaluators are key in making an immediate feedback on the writing outputs of the learners.
الهدف من التقييم التكويني هو مراقبة تعلم الطلاب لتوفير التغذية الراجعة المستمرة التي يمكن استخدامها من قبل المدرسين لتحسين التدريس ومن قبل الطلاب لتحسين تعلمهم. وبشكل أكثر تحديدًا ، التقييمات التكوينية:
مساعدة الطلاب على تحديد نقاط القوة والضعف لديهم والمجالات المستهدفة التي تحتاج إلى عمل
مساعدة أعضاء هيئة التدريس في التعرف على المواضع التي يعاني منها الطلاب ومعالجة المشكلات على الفور
التقييمات التكوينية بشكل عام منخفضة المخاطر ، مما يعني أنها ذات قيمة منخفضة أو لا قيمة لها على الإطلاق. تتضمن أمثلة التقييمات التكوينية مطالبة الطلاب بما يلي:
ارسم خريطة مفاهيم في الفصل لتمثيل فهمهم للموضوع
أرسل جملة أو جملتين تحدد النقطة الرئيسية للمحاضرة
تسليم اقتراح بحث للحصول على ردود فعل مبكرة