Social Assumptions and Consequences of Different Assessments:

Intelligence Testing:

Assumptions: Intelligence tests often assume a universal definition of intelligence based on cognitive abilities. They may prioritize certain skills (e.g., problem-solving, logical reasoning) over others (e.g., emotional intelligence, creativity).
Consequences for Learners:
Positive: Can identify academic potential and guide educational interventions for those with specific cognitive profiles.
Negative: May reinforce stereotypes or biases if tests are culturally biased or fail to account for diverse forms of intelligence.
Knowledge Testing:

Assumptions: Knowledge tests assume that mastery of specific content or skills reflects competence and readiness for roles or tasks.
Consequences for Learners:
Positive: Encourages mastery of subject matter and can serve as a reliable measure of acquired skills or qualifications.
Negative: Can promote rote memorization and discourage critical thinking or application of knowledge in novel contexts.
Example of Alternative Assessment: Project-Based Learning

Description:
Project-based learning (PBL) is an instructional approach where students actively explore real-world challenges and problems. Instead of traditional tests, students demonstrate knowledge and skills through the completion of projects or tasks.

Analysis:

Strengths:

Authentic Learning: Encourages application of knowledge in real-world scenarios, fostering deeper understanding.
Diverse Skills: Develops collaboration, communication, and problem-solving skills beyond academic content.
Personalization: Allows for individualized projects catering to students' interests and strengths.
Weaknesses:

Time-Intensive: Designing and implementing projects may require substantial time and resources.
Assessment Challenges: Grading can be subjective, requiring clear rubrics and criteria to ensure consistency.
Resource Equity: Access to resources and support outside the classroom can impact project outcomes.
Social Assumptions and Consequences:
Project-based learning challenges traditional assessment assumptions by emphasizing holistic skills and competencies beyond standardized testing. It promotes inclusivity by valuing diverse talents and backgrounds. However, its effectiveness can vary based on resources and support available to students, potentially exacerbating disparities in education.

In conclusion, alternative forms of assessment like project-based learning offer opportunities to address the limitations and biases associated with traditional intelligence and knowledge testing. By prioritizing real-world application and holistic skill development, these approaches can better prepare learners for success in diverse contexts. However, careful consideration of resources, equity, and assessment frameworks is essential to ensure meaningful and equitable learning experiences.

• User User

When we dive into the realm of learning analytics within educational settings, we're looking at a landscape that's as promising as it is complicated. The promise lies in the potential for a truly tailored educational experience. Imagine a classroom where learning is so personalized that each student is met exactly where they stand, intellectually and developmentally. This is what learning analytics can offer – a chance to revolutionize how we teach and learn by using data to sharpen the focus on individual needs and paths to success. The insights gleaned from this data can lead to better outcomes for students, as it empowers educators to refine their teaching to be more effective, and it enables interventions to be precisely timed to support students who might otherwise falter.

But as with any powerful tool, there are risks and challenges. The safeguarding of student data is a serious responsibility; it's not just about following laws, but about earning the trust of those whose information we're handling. Setting up the infrastructure to support learning analytics isn't trivial either – it demands significant investment, both in terms of technology and in the training required to use it effectively. There's a steep learning curve here for everyone involved.

And then there's the matter of interpretation. Data doesn't speak for itself; it needs to be read and understood in context. This means that educators and students alike must become adept at making sense of the information that's being collected. We must also remember that numbers don't capture everything. The qualitative aspects of learning – the discussions, the eureka moments, the personal breakthroughs – are harder to quantify and yet are essential to the full educational picture.

Lastly, we must be vigilant about equity. Data can be biased; it can reflect and amplify existing inequalities if we're not careful. So, as we implement learning analytics, we need to be as committed to fairness and inclusion as we are to innovation and efficiency. After all, the ultimate goal of education is to lift everyone up, not to inadvertently leave some behind.

@Sugam J Shivhare,@William Murphy

• Abdisamad Omar

Accessibility: One of the major advantages of educational technology for learning on the go is its accessibility. With the ubiquity of mobile devices such as smartphones and tablets, learners can access educational content at their convenience. Whether it's watching educational videos, participating in online discussions, or accessing digital textbooks, EdTech ensures that learning resources are readily available, removing constraints of time and location.

For example, platforms like Khan Academy and Coursera offer a wide range of courses that can be accessed on mobile devices. Learners can engage with content whenever they have a few spare minutes, whether it's during their daily commute or while waiting in line.@Rana Masarweh,@Sugam J Shivhare,@William Murphy,@Antonio F Bertachini A Prado,@Kiran Chaudhary,

• Mohammed Mohammed

التقييم والتقويم يندرج كلّ من التقييم والتقويم تحت قائمة العمليات الإدارية الاستراتيجية العصرية التي تلعب دوراً بارزاً في العملية الإدارية الكلية التي تهدف بصورة مباشرة إلى تحقيق غايات وأهداف المؤسسات والمنظمات العاملة في القطاعات المختلفة، حيث يمثلان المراحل الأخيرة في كلّاً من التخطيط الاستراتيجي والإدارة الاستراتيجية، ونجد أنّه قد لبس لدى فئة كبيرة من الأشخاص في التفريق بين هذين المفهومين، مما خلق حالة من الخلط بين المفاهيم، حيث إنّ هناك فرق واضح وشاسع بين كل منهما، ونظراً لذلك اخترنا أن نستعرض بشكل مفصل مفهوم كلاً منهما بشكل منفصل عن الآخر. مفهوم التقييم إنّ عملية التقييم هي عبارة عن نشاط إداري يقيس بدقة مدى تحقيق الأهداف والغايات المطلوبة، ويتمحور حول نشاطين رئيسين يتابعان عملية التنفيذ، ويرصدان الأخطاء فيها، ويقدمان تقريراً بذلك لاتخاذ القرار المناسب بشأنها، وتشكل المرحلة ما قبل الأخيرة من مراحل وضع استراتيجية إدارة الموارد البشرية، حيث نطرح في هذه المرحلة سؤال هل حققنا الهدف، وتتطلب الإجابة على هذا السؤال فحصاً دقيقاً لآلية العمل وخطواته، بصورة تضمن قياس الأداء الذي يتيح فرصة المقارنة الحقيقية بين الأداء المخطط له مسبقاً والأداء الفعلي، وتحديد الانحرافات. إعلان

إقرأ المزيد على موضوع.كوم: https://mawdoo3.com/%D9%85%D9%81%D9%87%D9%88%D9%85_%D8%A7%D9%84%D8%AA%D9%82%D9%8A%D9%8A%D9%85_%D9%88%D8%A7%D9%84%D8%AA%D9%82%D9%88%D9%8A%D9%85

• Mohammed Mohammed

كيف سيستفيد الطلاب من التقييم القائم على المعايير؟
مع التقييم القائم على المعايير، سيتم تصنيف الطلاب وفقا للعوامل الأكاديمية والسلوكية طوال العام الدراسي أو الفصل الدراسي. لا يتعلق مقياس الدرجات الجديد بتسجيل درجات (A إلي F)، ولكنه يهتم ب

التمكن (Mastery) والتقدم نحو الإتقان (progressing toward mastery) والبدأ في التقدم نحو الإتقان (beginning to progress toward mastery) ولم يُظهر التقدم بعد (not yet demonstrating progress)

سيتمكن الطلاب مما يلي:

إظهار موقف أكثر إيجابية وتحفيزا تجاه التعلم
تعلم أهمية التعلم المستقل
لمس قيمة المادة التعليمية
تجربة تحديات جديدة
الانخراط في الصف
التركيز على النجاح بدلا من الابتعاد عن الفشل فقط
ذلك أن العديد من المدارس في مختلف أنحاء العالم تبنت فكرة التقييم القائم على المعايير لعقود من الزمان. فيعد التقييم القائم على المعايير طريقة لعرض تقدم الطلاب بشكل شامل.

ومن المؤكد أنه مفيد للمعلمين حيث سيجدون أن تدريسهم في المكان الصحيح وأنه أكثر صلة بالموضوع. أما بالنسبة إلى الطلاب، فيعد التحفيز النتيجة النهائية العظيمة لهذا النظام والذي بدوره سيؤدي إلى نتيجة مثمرة.

• Mohammed Mohammed

Purdue University developed a learning analytics model called Course Signals, which uses data from online courses to predict learner success and provide early warnings and interventions.
There are several LA-based tools to report on educational activities, i.e., SNAPP uses comments and posts from discussion forums to visualize the interaction network among learners; GISMO visualizes online learning activities, or LOCO-Analyst summerizes and reports learning activities to instructors. However, most digital learning platforms do not automatically include the advanced tools required for applying LA. Additionally, utilizing these tools is too complex and have features (i.e., statistics) beyond the capability of classroom teachers. Moodle, an open sources and freely available LMS, is used extensively in higher education around the world. Therefore, we briefly describe two free LA dashboards for Moodle.

a) “MEAP: Moodle engagement Analytics Plugin” allows instructors to track students engagement based on their log activity, assessment data and posting to discussion forum (Liu et al., 2015).

b) “LEMO2 CourseExplorer” is an LA dashboard which visualizes students’ performance in both standard overview and customized format. Instructors can apply different filters to a specific learning objects and track daily activity flow, distribution of performance (quiz, forum, video-watching), test performance, calendar heatmap, peak activity in terms of time, etc., (Jaakonmäki et al., 2020).

c) “Inspire” is a Moodle Analytics API (application programming interface) that provides descriptive and predictive analytics engine by implementing machine learning backends (Romero and Ventura, 2020).
Notwithstanding the fact that LA has a great potential to support teachers and improve learning, there are a number of issues (and potential measures to ameliorate them) which should be considered in using LA:

1. Using LA to track digital traces and monitor students’ behavior raises serious question about data security, privacy, user protection and ethics. Enhancing LMS security, setting authorized access, informed consent of students, as well as clear instructions about how data will be stored, accessed, or used are among potential solutions.

2. LA dashboards provide instructors with a summary of students’ engagement. The decision and action to be taken are left to instructors or supporting staff. Drawing actionable knowledge from dashboards needs considerable Pedagogical Content Knowledge (PCK). This mandates extensive investment in Teacher Professional Development, both in pre-service and in-service programs. No matter how powerful or expensive an LA dashboard is, it can fulfill its potentials only through appropriate human judgement, decision and action.

3. Student-facing dashboards are claimed to foster reflection and Self-regulated Learning (SRL). However, visualization of activity per se may not automatically promote awareness in learners. Majority of LA experts believe students are unable to interpret what analytic dashboards are suggesting them or fail to take appropriate action (Winne, 2018).

4. Many scholars warned against too much reliance on teachers’ intervention based on LA dashboards due to the risk of reducing students’ autonomy and responsibility of taking charge of their own learning (Bodily and Verbert, 2017).

5. Quantitative, easy-to-display measures (i.e., presence, frequency or time spent) cannot fully portray a purposeful and deep engagement in learning. Dawson and Siemens (2014) called for “LA tools and techniques that go beyond surface-level analytics which uses diverse, alternative assessment methods which reflect twenty-first century education, a kind of complex and multimodal learning.” More qualitative indicators and multi-modal data types should be considered. For example, if a student spends less time on a task, it might be due to his superior background knowledge, better task management strategy or lack of engagement. LA can exploit Machine Learning and AI-based techniques, i.e., Natural Language Processing (NLP) and Deep Neural Networking (DNN), to distinguish between the quantity (frequently posting shallow comments in a forum) and the quality of engagement (deep, meaningful reasoning in a discussion forum). Furthermore, ML techniques build predictive models which facilitate timely intervention.

6. Finally, although LA tracks and monitors students activities in VLE, a great part of learning may happen offline: the time students spend reading textbooks, reflecting, solving problems, or completing a field project, contributes greatly to learning, especially to self-regulated learning, which can not be captured by LA. Therefore, not being active in online environments cannot be equated with a complete lack of learning. This might explain why some studies failed to find any clear correlational patterns between log data and final scores.

• Mai Tran

Embedded learning analytics holds the potential to revolutionize education by providing valuable insights into the learning process. These environments have the capability to improve teaching methods, personalize learning experiences, and enhance student outcomes. The potential benefits of embedded learning analytics include:

Personalized Learning: By analyzing student performance data, embedded learning analytics can identify the unique learning needs of each student. This information can be used to tailor instruction to match the individual needs and preferences of each learner, ensuring that they receive the most effective support and resources.

Adaptive Assessment: Through embedded learning analytics, educators can continuously assess student progress and adapt the learning environment to provide appropriate challenges and support. This adaptive approach can help students stay engaged and motivated by ensuring that they are consistently working at their optimal level of difficulty.

Real-Time Feedback: Embedded learning analytics can provide real-time feedback to both students and educators, allowing for timely adjustments to instructional strategies and resources. This immediate feedback can help students understand their strengths and weaknesses, while also providing teachers with valuable information to inform their teaching practices.

Data-Driven Decision Making: By analyzing large amounts of data, embedded learning analytics can help educators make informed decisions about curriculum design, resource allocation, and professional development. This data-driven approach can lead to more effective and efficient use of resources, ultimately improving student outcomes.

Collaborative Learning: Embedded learning analytics can facilitate collaboration among students and educators by providing a shared platform for exchanging ideas, resources, and feedback. This collaborative environment can help foster a sense of community and support among learners, leading to improved outcomes and more engaged learners.

Challenges in Creating and Implementing Environments with Embedded Learning Analytics
Despite the potential benefits of embedded learning analytics, there are several challenges that must be overcome to effectively implement these environments:

Privacy and Security Concerns: The collection and analysis of student data raises concerns about privacy and security. Educators and policymakers must work together to establish appropriate safeguards to protect student data and ensure that it is only used for legitimate educational purposes.

Ethical Considerations: As learning analytics becomes more sophisticated, it is essential to consider the ethical implications of using these tools to inform decisions about student outcomes. Educators must be cautious not to inadvertently discriminate against or stigmatize students based on their data, and ensure that the use of learning analytics is transparent and fair.

Technical Challenges: Implementing embedded learning analytics requires significant investment in technology infrastructure, including the development of sophisticated analytics tools, data storage and management systems, and user-friendly interfaces. Additionally, there must be ongoing maintenance and updates to ensure the accuracy and relevance of the analytics.

Professional Development: For embedded learning analytics to be effective, educators must be adequately trained in the use of these tools and in interpreting the data they provide. This may require significant resources for professional development, as well as ongoing support and collaboration between educators and technology developers.

Resistance to Change: Some educators may be resistant to adopting new technologies and pedagogical approaches, particularly if they perceive these changes as threatening their professional autonomy. It is essential to engage stakeholders in the design and implementation of embedded learning analytics environments to ensure widespread adoption and success.

Embedded learning analytics holds great potential to transform education by providing personalized, adaptive, and data-driven learning experiences. However, to fully realize these benefits, it is crucial to address the challenges associated with privacy, ethics, technology, professional development, and resistance to change. By overcoming these obstacles, embedded learning analytics can help create a more equitable and effective educational system for all students.

• Gift Wache Kanjedza

The potentials and challenges in creating and implementing environments with embedded learning analytics are that they should clearly judge the knowledge and understanding of the student on the basis of his/her performance on the digital environment. The challenge is to record assess and analyse the footprints of the student in the learning environment.
The learning and assessment environment that offers learning analytics is black board LMS in this online learning tool the students are judged and their performance is totally analysed by the ADMIN..

THIS SHOWS THAT POTENTIALS ARE MANY AND CHALLENGES ARE FEW CAN CAN BE CONTROLLED TO GET GREAT LEARNING ANALYTICS

• Syed Rubeena

Potentials of Embedded Learning Analytics:

Personalized Learning: Embedded learning analytics can provide insights into learners' progress, strengths, and weaknesses, enabling personalized learning experiences. By analyzing data on learners' interactions, performance, and preferences, educational environments can adapt and tailor content, resources, and interventions to meet individual needs.

Timely Feedback: Embedded learning analytics facilitate the delivery of immediate and ongoing feedback to learners. Real-time feedback allows learners to monitor their progress, identify areas for improvement, and make adjustments accordingly. It enhances self-regulation and supports continuous learning.

• Eqbal Mohammed

Good

• Mohammed Mohammed

Potentials :
educational technologies may simply serve to support the complex processes of social learning by recognizing and tracing the sociability of knowledge. The phenomenon of individual memory becomes less important Far less important than memory, now, is a learner’s capacity to navigate, discern and reassemble knowledge whose sources are acknowledged to be social.
There is a clearer view of the knowledge artefacts as it happens
First, assessment can now be readily embedded into learning. As a consequence, the traditional instruction/assessment distinction is blurred. Learning and assessment take place in the same time and space.
We can show the student their progress




Challenges:
Tests of memory and skill application anticipate replication of received knowledge, the same from one student to the next
To create learning programmes that are challenging and focused enough so as to create analytics from data that is significant not merely ‘traceable’. Not everything that is traceable is beneficial as learning analytics

• Eleni Protopapa

very helpful

• Eqbal Mohammed

It is amazing

• Eqbal Mohammed

Thanks a lot

• Eqbal Mohammed