Assessment Practice in e Learning Ecologies
The e-Learning Ecologies MOOC continues to improve its assessment practices by integrating the idea of multimodal meaning, especially in the design and implementation of quizzes like Computer Adaptive Testing (CAT) and Diagnostic Testing. This update acknowledges that assessment in digital learning environments goes beyond measuring knowledge with text-based questions. It focuses on understanding how learners create meaning using various modes of representation and interaction. Multimodal meaning involves using different modes—such as text, images, audio, video, symbols, and interactive elements—to support learning and assessment. In Diagnostic Testing, multimodality is essential for identifying learners’ prior knowledge, strengths, gaps, and misconceptions. Diagnostic quizzes in the MOOC may incorporate visual prompts, short video scenarios, diagrams, or audio instructions alongside written questions. These varied modes help learners from diverse backgrounds better grasp the task and express their knowledge, especially those who find text-heavy assessments challenging. Consequently, diagnostic testing becomes more inclusive, precise, and focused on learners. Computer Adaptive Testing in the e-Learning Ecologies MOOC also gains from multimodal meaning by adjusting not only the difficulty of questions but also the formats through which questions are presented. CAT systems respond to learners’ answers in real time, providing questions that fit their current understanding. When combined with multimodal elements—like interactive graphs, contextual images, or short explanatory clips—adaptive testing promotes deeper comprehension and lessens test anxiety. Learners perceive assessment as a guided learning experience rather than a strict evaluation. From an ecological perspective, these quiz designs support the MOOC’s focus on learner agency, personalization, and diversity. Multimodal diagnostic and adaptive quizzes recognize that learners think, interpret, and respond in varied ways. They also offer teachers and facilitators valuable data about how learners interact with content, allowing for more responsive teaching and meaningful feedback. Overall, integrating multimodal meaning into Computer Adaptive Testing and Diagnostic Testing changes quizzes into effective learning tools. Assessments become opportunities for exploration, reflection, and growth, reinforcing the e-Learning Ecologies MOOC’s commitment to fair, clear, and human-centered digital learning.
References
• Cope, B., & Kalantzis, M. (2015). A pedagogy of multiliteracies: Learning by design. Palgrave Macmillan.
• Kalantzis, M., Cope, B., & Pinheiro, P. (2020). Learning ecosystems and e-learning ecologies. Routledge.

• Jovilyn Fernandez

Learning analytics
Learning analytics is one of the most transformative concepts in recursive feedback systems within digital learning environments. It refers to the process of collecting, analyzing, and interpreting data about learners’ interactions in online platforms in order to improve teaching, personalize learning, and support continuous assessment. As defined by Siemens and Baker (2012), learning analytics provides “actionable insights” that help educators identify patterns, predict learner performance, and intervene at the right moment. In other words, learning analytics makes feedback immediate, adaptive, and deeply informed.

One practical example of learning analytics can be seen in platforms like Moodle, Coursera, or Canvas. These systems track how long students spend on a lesson, which activities they struggle with, how they perform on quizzes, and how actively they participate in discussions. A teacher can view dashboards that highlight who is falling behind, which concepts need reinforcement, or what types of materials lead to better engagement. Instead of waiting until the end of a unit to give feedback, instructors can act early and provide targeted guidance. This is exactly what recursive feedback means: constant loops of information that help learners improve throughout the learning process rather than after it.

Learning analytics also empowers students by giving them visual dashboards that show their progress, completion rates, time spent, and mastery levels. These insights support self-regulation and metacognition, helping learners identify their strengths and weaknesses. For example, students on Duolingo or Khan Academy receive instant performance feedback along with suggestions on what to study next, making the learning experience adaptive and personalized.

Additionally, learning analytics contributes to more equitable assessment because it focuses on patterns of performance rather than one-time tests. As Cope and Kalantzis (2017) argue, digital learning environments allow assessment to become continuous, diagnostic, and criterion-referenced rather than norm-referenced. Instead of comparing students against each other, analytics helps measure how well each learner meets specific learning goals.

Ultimately, learning analytics embodies the essence of recursive feedback: ongoing data-informed loops that enhance learning, guide teaching, and create more responsive and personalized educational environments.

References
– Siemens, G., & Baker, R. (2012). Learning Analytics and Educational Data Mining: Towards Communication and Collaboration.
– Cope, B., & Kalantzis, M. (2017). e-Learning Ecologies: Principles for New Learning and Assessment.
– Long, P., & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review.

• Ghizlane Rahmani

Great work! It was very informative to read your update.

• Pratiksha Phukan

The concept of Computer Adaptive Testing (CAT) is a revolutionary approach to assessment that customizes the test experience in real-time to match the individual ability level of the test taker.

Thus making it adaptive, data-driven assessment method that uses an algorithm and an item bank, grounded in Item Response Theory, to personalize the difficulty of the test in real-time. Its primary goal is to maximize the accuracy of the ability estimate while minimizing hte number of questions administered.

• Maria Janine Herrera

One emerging concept in recursive feedback is learning analytics. Learning analytics refers to the collection, measurement, and analysis of student data to understand and optimize the learning process (Siemens & Long, 2011). Unlike traditional feedback methods that rely mainly on teacher evaluations, learning analytics uses digital traces left by students in online platforms—such as time spent on tasks, quiz results, discussion participation, and resource usage—to provide real-time insights.

The recursive aspect of learning analytics lies in its continuous feedback loop. Data is captured as learners interact with digital platforms, analyzed to identify trends or challenges, and then used to provide feedback to both students and instructors. This allows learners to reflect on their performance while teachers can adjust instructional strategies based on evidence rather than assumptions.

A practical example of learning analytics in action is the use of dashboards in Learning Management Systems (LMS) like Canvas, Moodle, or Blackboard. These dashboards display data such as progress bars, grades, and activity logs, giving students a clear picture of their learning journey. Instructors can also use this data to identify at-risk students who may need additional support or to recognize high-performing students who may benefit from advanced challenges.

Another example is Massive Open Online Courses (MOOCs), where platforms like Coursera or edX rely heavily on analytics to track learner engagement, predict dropout risks, and suggest personalized learning pathways. In this sense, analytics not only supports individual learning but also informs instructional design at a larger scale.

In conclusion, learning analytics is a powerful recursive feedback tool because it makes the learning process more transparent, adaptive, and data-driven. By transforming raw data into actionable insights, it fosters self-regulated learning for students and evidence-based teaching for educators, ultimately improving learning outcomes.

Reference:
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30–32.

• Roselle Ty

One important concept in recursive feedback is formative assessment. Unlike summative assessments that evaluate learning at the end of a unit, formative assessment is ongoing and designed to provide continuous feedback to both learners and instructors during the learning process. According to Black and Wiliam (1998), formative assessment is central to improving student achievement because it helps learners identify their strengths and areas for growth while allowing teachers to adapt instruction in real time.

Formative assessment is recursive in nature because it creates a feedback loop. Students demonstrate their understanding through small tasks such as quizzes, reflections, or group activities. Teachers then analyze the results, provide targeted feedback, and adjust instruction accordingly. Students, in turn, use the feedback to improve their performance on subsequent tasks. This cycle ensures that learning is not static but constantly evolving through iterative improvement.

A practical example of formative assessment in practice is the use of exit tickets in classrooms. At the end of a lesson, students answer a brief question about what they learned or found difficult. Teachers quickly review these responses and use them to shape the next lesson. In e-learning environments, tools like Google Forms or LMS-based quizzes serve a similar purpose, giving both instant feedback to learners and valuable diagnostic insights to instructors.

Another example is the use of peer assessment in writing courses. Students provide comments on each other’s drafts, offering constructive criticism while also learning by analyzing their peers’ work. This process builds a recursive cycle of reflection, revision, and improvement.

In conclusion, formative assessment embodies the principles of recursive feedback by making learning an iterative process. Instead of waiting until the end to measure success, it ensures that learners are continuously supported, guided, and empowered to take ownership of their growth.

Reference:
Black, P., & Wiliam, D. (1998). Inside the Black Box: Raising Standards Through Classroom Assessment. Phi Delta Kappan, 80(2), 139–148.

• Roselle Ty

One important concept in recursive feedback is formative assessment. Unlike summative assessments that evaluate learning at the end of a unit, formative assessment is ongoing and designed to provide continuous feedback to both learners and instructors during the learning process. According to Black and Wiliam (1998), formative assessment is central to improving student achievement because it helps learners identify their strengths and areas for growth while allowing teachers to adapt instruction in real time.

Formative assessment is recursive in nature because it creates a feedback loop. Students demonstrate their understanding through small tasks such as quizzes, reflections, or group activities. Teachers then analyze the results, provide targeted feedback, and adjust instruction accordingly. Students, in turn, use the feedback to improve their performance on subsequent tasks. This cycle ensures that learning is not static but constantly evolving through iterative improvement.

A practical example of formative assessment in practice is the use of exit tickets in classrooms. At the end of a lesson, students answer a brief question about what they learned or found difficult. Teachers quickly review these responses and use them to shape the next lesson. In e-learning environments, tools like Google Forms or LMS-based quizzes serve a similar purpose, giving both instant feedback to learners and valuable diagnostic insights to instructors.

Another example is the use of peer assessment in writing courses. Students provide comments on each other’s drafts, offering constructive criticism while also learning by analyzing their peers’ work. This process builds a recursive cycle of reflection, revision, and improvement.

In conclusion, formative assessment embodies the principles of recursive feedback by making learning an iterative process. Instead of waiting until the end to measure success, it ensures that learners are continuously supported, guided, and empowered to take ownership of their growth.

Reference:
Black, P., & Wiliam, D. (1998). Inside the Black Box: Raising Standards Through Classroom Assessment. Phi Delta Kappan, 80(2), 139–148.

• Roselle Ty

One important concept in recursive feedback is formative assessment. Unlike summative assessments that evaluate learning at the end of a unit, formative assessment is ongoing and designed to provide continuous feedback to both learners and instructors during the learning process. According to Black and Wiliam (1998), formative assessment is central to improving student achievement because it helps learners identify their strengths and areas for growth while allowing teachers to adapt instruction in real time.

Formative assessment is recursive in nature because it creates a feedback loop. Students demonstrate their understanding through small tasks such as quizzes, reflections, or group activities. Teachers then analyze the results, provide targeted feedback, and adjust instruction accordingly. Students, in turn, use the feedback to improve their performance on subsequent tasks. This cycle ensures that learning is not static but constantly evolving through iterative improvement.

A practical example of formative assessment in practice is the use of exit tickets in classrooms. At the end of a lesson, students answer a brief question about what they learned or found difficult. Teachers quickly review these responses and use them to shape the next lesson. In e-learning environments, tools like Google Forms or LMS-based quizzes serve a similar purpose, giving both instant feedback to learners and valuable diagnostic insights to instructors.

Another example is the use of peer assessment in writing courses. Students provide comments on each other’s drafts, offering constructive criticism while also learning by analyzing their peers’ work. This process builds a recursive cycle of reflection, revision, and improvement.

In conclusion, formative assessment embodies the principles of recursive feedback by making learning an iterative process. Instead of waiting until the end to measure success, it ensures that learners are continuously supported, guided, and empowered to take ownership of their growth.

Reference:
Black, P., & Wiliam, D. (1998). Inside the Black Box: Raising Standards Through Classroom Assessment. Phi Delta Kappan, 80(2), 139–148.

• Roselle Ty

En la actualidad, hablar de enfoques innovadores en la enseñanza y el aprendizaje implica reconocer que la educación se desarrolla en un ecosistema digital en constante transformación. Las metodologías tradicionales, centradas en la transmisión unidireccional del conocimiento, están siendo reemplazadas por prácticas que priorizan la interacción, la colaboración y la construcción activa del saber. La era digital no solo ofrece herramientas, sino que exige repensar el rol de docentes y estudiantes dentro del proceso educativo.

Uno de los enfoques más destacados es el aprendizaje basado en proyectos (ABP), potenciado por plataformas digitales que permiten a los estudiantes trabajar de forma colaborativa, incluso a distancia. Este enfoque fomenta la resolución de problemas reales, integrando diversas competencias y utilizando recursos multimodales como presentaciones interactivas, infografías, videos y simulaciones.

Otro enfoque clave es el blended learning o aprendizaje híbrido, que combina la enseñanza presencial con experiencias virtuales. Esta modalidad ofrece flexibilidad, personalización y acceso a múltiples fuentes de conocimiento. En este marco, cobran relevancia las plataformas de gestión del aprendizaje (LMS), que facilitan la organización de contenidos, la evaluación en línea y la retroalimentación inmediata.

Además, el auge de la inteligencia artificial y el análisis de datos educativos abre la puerta a una enseñanza adaptativa, capaz de identificar las necesidades particulares de cada estudiante y ajustar los recursos de acuerdo con su ritmo y estilo de aprendizaje. Esto se complementa con herramientas de gamificación, que convierten el aprendizaje en una experiencia motivadora al incorporar dinámicas propias de los juegos.

Por otro lado, la realidad aumentada y la realidad virtual permiten experiencias inmersivas, donde los estudiantes pueden explorar entornos imposibles en la vida cotidiana, desde visitar ruinas arqueológicas hasta realizar prácticas médicas simuladas.

En conclusión, los enfoques innovadores de la era digital no se limitan a usar nuevas tecnologías, sino a redefinir la enseñanza para que sea participativa, personalizada y significativa. El desafío está en garantizar que estas herramientas se utilicen con una intención pedagógica clara, orientada a preparar a los estudiantes para desenvolverse en un mundo complejo, interconectado y en permanente cambio.

• Ana Ontaneda

Recursive feedback refers to a continuous, iterative process of giving and receiving feedback that allows learners to improve their understanding and skills progressively. Unlike a one-time evaluation, recursive feedback encourages reflection, adjustment, and re-engagement with the material, creating a loop where learning is constantly refined.

Example in Practice:
A common implementation of recursive feedback is through formative assessment. For instance, in a writing course, students submit a draft essay. The instructor provides detailed comments on structure, argumentation, and clarity. Students revise their essays based on the feedback, resubmit, and may receive further comments. This cycle can repeat multiple times, helping students gradually refine their skills and understanding.

Why It Matters:
Recursive feedback supports deep learning, fosters metacognition, and helps learners develop self-regulatory skills. It ensures that feedback is actionable and not just evaluative.

References & Resources:

Brookhart, S. M. (2017). How to Give Effective Feedback to Your Students. ASCD. Link

Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. PDF

Visual/Media Suggestion:

Diagram showing the recursive loop: Draft → Feedback → Revision → Reflection → New Draft

Short video demonstrating peer-to-peer recursive feedback in a classroom

Call to Engagement:
I invite participants to share how they have applied recursive feedback in their courses. How has iterative feedback helped students improve learning outcomes?

• Fernanda Leon Jara