How can teachers use learning analytics to make informed decisions?

Start by clearly defining what you want to achieve with learning analytics. Determine the specific learning objectives and outcomes you aim to impact. Whether it's improving student engagement, identifying at-risk students early, or personalising learning experiences, having clear goals will guide your use of data effectively.

Teachers can use learning analytics to make informed decisions by first collecting and analyzing data on student performance and engagement. This data can provide valuable insights into students' learning needs and preferences. Teachers can then use this information to personalize instruction, identifying areas where students may need additional support or challenge. Additionally, learning analytics can help teachers track student progress over time, allowing them to adjust their teaching strategies accordingly.

Teachers can leverage learning analytics to make informed decisions by analyzing data on student engagement, performance, and behavior. By tracking metrics such as completion rates, quiz scores, and time spent on tasks, educators can identify struggling students, tailor instruction to individual needs, and intervene when necessary. Additionally, analyzing trends over time can inform instructional design improvements and curriculum adjustments. By harnessing the power of learning analytics, teachers can optimize learning experiences and maximize student success.

I use analytics on student performances with two goals in mind. The first one is performed right after administering a quiz. The quiz app generates analytics on the overall student performances for each quiz question. The analytics are projected on the big screen for students to view. I would then review each question correcting on misunderstanding and mistakes, and give appropriate feedback to students. The second goal is performed post-lecture where I use the analytics from a series of quizzes to explore patterns in student performances. Data do help me in improving my teaching practices, which become a part of action research.

In leveraging learning analytics for informed decision-making, educators must carefully select data sources. These include data from the Learning Management System (LMS) to track engagement and progress, assessment data to gauge understanding, and clickstream data to monitor interactions with course content. Additionally, gathering student feedback and incorporating external data sources like demographics or prior performance enriches contextual understanding. By analyzing this comprehensive data, educators can tailor interventions, enhance teaching strategies, and optimize learning experiences effectively.

Learning Management System (LMS) data provides real-time insights to student activities and progress, and is often critical for learning analytics to help teachers. Data from a Student Information System (SIS) can also be critical in identifying learners with specific challenges or goals. If synchronous web conferencing tools are used, detailed data from these systems can also help to clarify learner engagement patterns. Attendance tracking tools also have valuable data, if used. New research suggests that patterns of engagement can be detected in audio levels in the classroom and other multi-mode sources. Well-designed self-reported data like surveys and questionnaires can be helpful if consistently gathered early in all courses.

Descriptive analytics is most useful if it includes tools to summarize and interpret data, such as visualizations and calculations based on evidence-backed metrics. Look for tools that have open formulas, especially those that allow the formulas and thresholds to be edited. Predictive analytics using machine learning will require historical data to train the model. Currently these models are most accurate if trained on an institution's own data. If you do not have in-house data scientists, look for tools that offer those services. Prescriptive analytics require extensive annotation of learning resources, e.g. tagging all course content with competencies. New AI tools may help to partially automate this process in the near future.

El acceso a datos sobre la dedicación y el empeño de estudiantes en las experiencias de aprendizaje es da capacidad de acción a docentes y administradores. Analizando los datos de las herramientas de tecnología educativa, es posible identificar patrones y adaptar los métodos de enseñanza acorde a las necesidades de los alumnos. Esto incluye la expansión de secciones que han sido bien recibidas, la simplificación de temas que han resultado en abandono o retrasos en el progreso, y la detección precoz de alumnos que requieren apoyo adicional.

Tools may be reactive or proactive. Reports and dashboards are reactive -- teachers and others must view them and interpret results, then decide what action to take. Notification systems can proactively contact teachers, learners, advisors, and others. These tools must be used with caution. When teachers view reports or dashboards or receive notifications, remember they provide a limited view of student activity based on selected data sources and analysis. Avoid generalizing student ability or potential based on this limited view. Be especially careful in triggering notifications to learners. Most learners are already aware of their challenges. Make messages supportive and offer constructive actions and resources available to learners.

En el caso de ODILO, una herramienta de tecnología educativa que se amolda a las necesidades de cada institución, cuenta con un sistema BI, que hace un seguimiento completo de todo lo que realiza el alumno en la plataforma, incluido el consumo de títulos educativos de expansión curricular. Dando la oportunidad a los administradores y docentes la oportunidad real de conocer no solo el aprendizaje formal del alumno, si no, también la no formal. Alcanzando un conocimiento íntegro del aprendizaje real del alumno.

Any implementation of a learning analytics system should be followed up with an evaluation plan. Measure how often the tools are being used and results. If possible, start with a pilot and compare results between a group using the system and a similar control group. Throughout the use of a learning analytics system, track the outcomes and actions expected to occur as a result of the system. For example, measure referrals and usage of a tutoring center, and correlate that with course and retention outcomes. It is especially important to gather data from teachers using a learning analytics system to assess accuracy, usability, and actual use. Implementation of the LA system should be flexible and responsive to the needs of the teachers.

A análise de aprendizagem é o processo de coleta e análise de dados sobre o aprendizado dos alunos para identificar áreas de força e fraqueza, personalizar o ensino e tomar decisões mais informadas sobre a instrução. Os professores podem usar a análise de aprendizagem de diversas maneiras para tomar decisões certeiras.

Learning analytics serves as the compass and map, guiding teachers through the labyrinth of pedagogy. By delving into this trove of data, educators can uncover the hidden narratives of each learner’s journey, spotting the crossroads where some may linger or stray. These insights, like starlight piercing through the night, illuminate the path for tailored interventions, enabling the sage educator to craft experiences that resonate with every soul in their care. By observing the patterns of engagement and understanding, teachers can weave a more inclusive tapestry of learning, one that honors the diverse tapestries of understanding that each student brings to the classroom.