Возможности и проблемы при создании и внедрении сред со встроенной аналитикой обучения
Среды обучения с аналитикой обучения (Learning Analytics) используют данные о взаимодействии учащихся с образовательными материалами, платформой или друг с другом. Это позволяет персонализировать обучение, повышать эффективность и предоставлять обратную связь. Однако внедрение таких систем сопряжено с рядом вызовов.
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Возможности
1. Персонализация обучения:
o Системы с аналитикой могут адаптировать содержание и задания к индивидуальным потребностям учащихся.
o Пример: платформы предлагают дополнительные материалы на основе анализа слабых мест.
2. Раннее выявление проблем:
o Аналитика помогает выявить учащихся, которые могут столкнуться с трудностями (например, низкая активность на платформе, пропуски сроков).
o Преподаватель может вмешаться до того, как учащийся отстанет.
3. Повышение вовлеченности:
o Анализ паттернов поведения студентов позволяет разрабатывать стратегии для повышения мотивации.
o Например, геймификация на основе активности учащегося.
4. Обоснованное принятие решений:
o Данные об успеваемости и активности помогают администраторам и преподавателям корректировать учебные программы и подходы.
5. Прозрачность прогресса:
o Учащиеся видят собственный прогресс через панели визуализации (дашборды), что способствует их мотивации и самоанализу.
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Проблемы
1. Этика и конфиденциальность:
o Сбор и анализ больших объемов данных о поведении учащихся требует защиты их приватности.
o Необходимы четкие политики по управлению данными и согласие учащихся.
2. Техническая сложность:
o Разработка систем с аналитикой требует значительных ресурсов, технологий и экспертизы.
o Важно обеспечить интеграцию таких систем с другими образовательными платформами.
3. Риск предвзятости:
o Алгоритмы могут некорректно интерпретировать данные, создавая ложные выводы о студентах.
4. Проблемы интерпретации данных:
o Преподаватели и администраторы могут неправильно интерпретировать сложные данные аналитики, что приведет к ошибочным решениям.
5. Сопротивление изменениям:
o Учащиеся и преподаватели могут опасаться использования аналитики, считая ее инструментом контроля, а не помощи.
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Обновление: Пример среды обучения с аналитикой
Платформа: Coursera
Как работает:
• Coursera собирает данные об обучающихся: время, проведенное на платформе, прогресс в курсах, результаты тестов, поведение при выполнении заданий.
• Использует аналитику для:
o Предоставления персонализированных рекомендаций (например, вот курсы, которые вам могут быть интересны).
o Определения сложности материалов и их соответствия разным категориям студентов.
o Создания отчетов для преподавателей о том, какие темы вызывают трудности у большинства студентов.
Эффекты:
1. Положительные:
o Учащиеся получают точную обратную связь в реальном времени.
o Преподаватели могут оперативно корректировать курсы, добавляя разъяснения сложных тем.
o Платформа мотивирует студентов за счет наглядного прогресса.
2. Проблемы:
o Не всегда очевидно, как принимать решения на основе данных (например, стоит ли изменять курс, если у 20% студентов есть затруднения).
o Студенты могут чувствовать себя под цифровым надзором, что снижает их комфорт.
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Итог
Аналитика обучения обладает огромным потенциалом для улучшения образовательного процесса, но требует грамотного внедрения. Успех таких систем зависит от того, как соблюдаются этические нормы, обеспечивается доступность данных и внедряется поддержка для всех участников образовательного процесса.

• Gaukhar Kodasheva

An advantage to embedded learning analytics (LA) is that they can be used seamlessly to support metacognitive monitoring during participation in the learning activity itself. However, a weakness of being embedded is the possibility of being ignored; i.e., there is no reason to assume that students will naturally use the analytic affordances of the tool to support their participation individually or as a group. For this reason, such use needs to be specifically encouraged by structuring it in to the learning activity parameters.
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. This platform allow:
a) “MEAP: Moodle engagement Analytics Plugin” allows instructors to track students engagement based on their log activity, assessment data and posting to discussion forum.
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.
c) “Inspire” is a Moodle Analytics API (application programming interface) that provides descriptive and predictive analytics engine by implementing machine learning backends.

• Nguyễn Phương

Based on video discussion, learning analytics involves the integration of data collection and analysis tools directly into learning environments. Though it offers various significant potential benefits, it also presents certain challenges.
By implementing learning analytics, students' learning process can be personalized, tailoring instruction based on their needs and paces. Also, it can provide immediate feedback which helps to identify at-risk students having knowledge gaps and address it by integrating intervention. Lastly, it can improve teaching practices where they can gain insights into student learning, inform their instructional decisions, and make data-driven decisions about curriculum, learning materials, and policies.
Despite its potential benefits, embedded learning analytics raises concerns about privacy, security, and technical complexity. Protecting student data requires robust measures while implementing and maintaining analytics systems can be resource-intensive. Introducing new technologies may face resistance from educators and administrators who are unfamiliar with their use, thus they need training about it. Hence, excessive reliance on data can overshadow other crucial aspects of education, such as student engagement and motivation.

• Cindy Deguito

In the digital age, using learning analytics in education has great potential but also comes with challenges. As a college math teacher, I see how these tools can help us understand and improve student learning.

Learning analytics can track how students interact with course materials, like watching videos or completing assignments. This data helps us see where students struggle and where they excel. For example, if many students get a particular math problem wrong, I can review that topic in class. Learning analytics can also personalize learning by suggesting resources based on each student’s needs, making learning more effective and engaging.

However, there are challenges too. One major issue is privacy. We need to ensure that students’ data is protected and used ethically. Another challenge is accessibility. Not all students have the same access to technology, which can create inequalities. Additionally, implementing these systems requires significant resources and training for teachers to use the data effectively. There’s also the risk of over-reliance on data, which might overlook the human aspects of teaching and learning.

In summary, while learning analytics can greatly enhance education by providing insights and personalized learning, we must address privacy, accessibility, and resource challenges to use these tools effectively and fairly.

• Cymber Orvie Quevedo

Creating classrooms with embedded learning analytics in mathematics can really help students learn better by giving teachers real-time data on how each student is doing. This means teachers can adjust their teaching and provide support to students who are struggling right away. However, there are some challenges. For example, we need to be careful about student privacy and make sure that their data is safe. Teachers also need training to understand the data and use it wisely, without losing sight of each student’s unique needs. Plus, setting up these systems can require a lot of resources, which can be hard for schools with limited budgets. Overall, while using learning analytics has great potential to improve learning, we must be mindful of these challenges to make it work effectively.

• Sarah Jean Cole

One of the primary advantages of embedded learning analytics is the ability to provide real-time feedback to both learners and educators. This immediate insight allows students to understand their progress and areas for improvement, fostering a more personalized learning experience. By analyzing data related to student performance and engagement, educators can tailor their instructional strategies to meet individual needs, ensuring that support is provided where it is most needed. Furthermore, the integration of learning analytics can enhance collaboration among students by highlighting areas where peer support may be beneficial, thereby promoting a more interactive and community-driven learning environment.

Additionally, embedded learning analytics can facilitate the development of adaptive learning technologies. These systems can dynamically adjust content delivery based on a learner's performance, providing more challenging material when appropriate or revisiting foundational concepts as needed. This adaptability can help maintain student motivation and engagement by creating a more relevant and responsive educational experience. Furthermore, the data generated through these analytics can inform institutional decision-making, helping schools and universities refine curricula and improve overall educational outcomes based on empirical evidence.

Despite these promising potentials, the implementation of embedded learning analytics also poses several challenges. One significant concern is the ethical use of data. Collecting and analyzing student data raises questions about privacy, consent, and the potential for misuse. Institutions must ensure that robust policies are in place to protect student information and to use data responsibly. Additionally, there is a risk of over-reliance on data-driven metrics, which may overlook the qualitative aspects of learning, such as creativity and critical thinking. Educators must balance the insights gained from analytics with a holistic understanding of student development.

Another challenge is the need for adequate training and support for educators. Many teachers may lack the necessary skills or knowledge to interpret and utilize analytics effectively. Professional development programs must be established to equip educators with the tools and understanding needed to integrate analytics into their teaching practices. Moreover, the technology infrastructure must be robust and accessible, which can be a significant hurdle for some institutions, particularly those with limited resources. Ensuring that all stakeholders—educators, students, and administrators—are on board and well-informed about the benefits and functionalities of embedded learning analytics is crucial for successful implementation.

In summary, while the potential for embedded learning analytics to transform educational environments is significant, careful consideration of the associated challenges is essential. By addressing ethical concerns, providing adequate training, and ensuring a balanced approach to data use, educators and institutions can harness the power of learning analytics to enhance student learning and institutional effectiveness. The journey towards integrating these analytics into educational settings will require collaboration, innovation, and a commitment to prioritizing student success and well-being.
@Marynel Comidoy,@Cindy Deguito,@Michael Llagas,@Anna Marie Pelandas,

• Christian Jay Fajartin

The Future of Education: Embedded Analytics

Embedded learning analytics can help teachers better understand how their students are learning and make adjustments to their teaching. It can also help students learn more effectively and efficiently.
In our school we make use of Blackboard LMS and QUIPPER LMS
• Blackboard: Teachers can use Blackboard's analytics features to see how often students log in, how long they spend on assignments, and how they perform on quizzes. This information can help teachers identify students who may need extra support or adjust their teaching methods.
• Quipper LMS: Quipper offers analytics that track student progress on assignments, quizzes, and other activities. Teachers can use this data to identify areas where students may be struggling and provide targeted support.
Benefits of using embedded learning analytics in schools:
• Personalized learning: Teachers can tailor instruction to meet the needs of individual students.
• Improved assessment: Embedded analytics can provide teachers with real-time feedback on student progress.
• Data-driven decision making: Schools can use data to make informed decisions about curriculum, resources, and teaching methods.
• Early intervention: Teachers can identify students who may be struggling and provide support before they fall behind.
Challenges and considerations:
• Technical issues: Schools may need to invest in technology and training to effectively use embedded learning analytics.
• Data privacy: Schools must ensure that student data is collected and used ethically and securely.
• Equity: Not all students may have equal access to technology or the skills needed to benefit from embedded learning analytics.
To maximize the benefits of embedded learning analytics, schools can:
• Invest in technology: Ensure that teachers and students have access to reliable internet and devices.
• Provide training: Offer professional development opportunities for teachers on how to use and interpret learning analytics data.
• Develop data privacy policies: Implement measures to protect student data.
• Consider equity: Ensure that all students have equal access to technology and support.
By addressing these challenges and leveraging the potential of embedded learning analytics, schools can create more effective and equitable learning environments for their students.

@May Flor Castillo,@Joan Valery Espinosa,@Michael Llagas,@Stephanie Pablo,

• Marynel Comidoy

Creating and implementing environments with embedded learning analytics holds significant potential but faces numerous challenges. On the other side, personalized learning can be achieved through adaptive learning paths, tailoring educational content to students' needs, and providing targeted interventions for those struggling, thereby improving learning outcomes. Enhanced engagement is possible through interactive feedback and gamification, making learning more engaging and enjoyable. Data-driven decision-making enables continuous improvement in curriculum and effective resource allocation.

At the same time, predictive analytics can be used for early warning systems and performance forecasting, helping identify at-risk students early and setting realistic goals. However, these opportunities come with challenges, including concerns over privacy and security of student data, compliance with data protection regulations, technical issues like integration and scalability, and ethical considerations regarding bias, fairness, and transparency.

Additionally, implementation and adoption hurdles such as the need for educator training, resistance to change, and the costs associated with technology investment, training, and maintenance present significant obstacles. Despite these challenges, implementing embedded learning analytics requires a balance between the benefits of personalized, data-driven education and the need for ethical data use, ensuring privacy, fairness, and sustainability to harness the analytics powerfully.

• Anna Marie Pelandas

LEARNING ANALYTICS: Challenges and Opportunities BY: YI-SHAN TSAI

What are learning analytics?
Learning Analytics (LA) are commonly defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”. In essence, LA analyses and uses data trails produced by learners in learning environments to generate information about learners’ progress and learning patterns. Such information can further be used as a basis for prediction, intervention, personalisation, and adaptation, such as suggesting learning tips or materials and redesigning curricula.
THE INFORMATION ABOUT IS TAKEN FROM THE AUTHOR NAME YI-SHAN TSAI , A Lecturer in the Centre for Learning Analytics at Monash (CoLAM) at the Faculty of Information Technology, Monash University. Yi-Shan’s research aims to enhance learning, teaching, and educational environments with the adoption of digital and data technologies. YOU CAN WATCH HER VIDEO ABOUT THIS TOPIC IN THIS LINK: https://www.solaresearch.org/2021/03/learning-analytics-3-challenges-and-opportunities/
@Marynel Comidoy,@Stephanie Pablo,@Joseph Rizon,@Cindy Deguito,

• May Flor Castillo

Regarding alternative forms of assessment, an example is project-based assessment. In this approach, students are evaluated based on their completion of a real-world project or task that demonstrates their application of knowledge and skills. This form of assessment allows for more authentic evaluation of student abilities and encourages deeper learning.

Educational evaluation is important to assess the effectiveness of educational programs and initiatives. One example of educational evaluation is a pre-post test design to measure the impact of an intervention on student learning outcomes. This approach involves assessing students' knowledge or skills before and after a specific intervention to determine its effectiveness. A strength of this approach is its ability to measure changes in student performance directly related to the intervention. However, a weakness is that it may not account for other factors that could influence student outcomes.

• Hiba Ahmed Mohammed Khamis