Title: LA-based interventions to improve learners’ performance 

University: Faculty of Engineering, University of San Carlos of Guatemala, Guatemala (Oliva-Córdova et al., 2021)

Section of the framework: LA generate data on student – teacher, student – student and student - content interventions

What competencies and learning outcomes of the DigicompEdu Framework are we addressing?

Competencies

Learning outcomes

Self-regulated learning

Analysing evidence

Feedback and planning

- To use digital technologies to allow learners to collect evidence and record progress, e.g. audio or video recordings, photos.

- To use digital technologies (e.g. ePortfolios, learners’ blogs) to allow learners to record and showcase their work.

- To analyse and interpret available evidence on learner activity and progress, including the data generated by the digital technologies used.

- To use digital technology to grade and give feedback on electronically submitted assignments.

 

Key issues:

This best practice based on Oliva-Córdova et al. (2021), emphasises that learning analytics help answer the following issues:

●       How practical is a course?

●       Is the course meeting the needs of the students?

●       How can the needs of the students be supported?

●       What type of interactions are engaging and productive?

To answer these questions, it is necessary to review and assess learners’ learning behaviour, their grades, the drop-out rate and the teacher's reflections at the end of the course. In such a way, LA can help to analyse student-student, student-teacher-student, and student-content interventions.

This best practice shows what kind of data teachers can collect to inform teaching and learning interventions  (table 1).


Table 1. Data collecting method in relation to data analysis goal (based on Oliva-Cordoba, Garcia-Cabot & Amado-Salvatierra, 2021, p. 9)

Suggested independent variable / data analysis goal

Data collecting method / data that can be monitored and analysed to learn of the specific variable

Total studying time in LMS

Calculating the total number of hours employed between login and logout.

Interaction with virtual learning objects

Adding up the total interactions reported in the SCORM report of the virtual learning objects.

Interaction in forums/wiki

Adding up the total reported interactions with the forum analytics plugin.

Interaction with learning tasks

Adding up the total number of participants’ task submissions through the submission distribution block.

Learning Performance

Adding up all scores for assignments and assessments in the course.

Communication and feedback

Counting the number of messages and task feedback.

Mentoring

Counting the number of follow-up emails, phone calls, synchronous meetings, and participation in forums of questions.

Learning Design

Counting the number of resources designed for the course, readjusted educational resources, enriched videos, complementary readings, and designed extracurricular activities.

Motivation

Counting the number of motivation messages sent, extracurricular meetings, and game dynamics implemented.


This best practice shows that communication and feedback affect learning performance and that the role of teachers is crucial. They have to design the learning environment properly, they have to be able to manage LA tools, provide timely feedback and monitor learning performance. Students’ performance improves after LA-based interventions made by teachers.


Relevance for teachers:

LA-based interventions regard the content to deliver, the pedagogical strategies selected, the design of the didactic sequences, the type of assessment to choose, the tool to support the design. All these elements should be customizable and user-friendly. LA-based learning design has a direct positive impact on students’ learning outcomes. In other words, when teachers have an active role in learning design, students’ learning outcomes and performance improve.


References

Oliva-Córdova, L. M., Garcia-Cabot, A, & Amado-Salvatierra, H. R. (2021) Application of Learning Analytics in Virtual Tutoring: Moving toward a Model Based on Interventions and Learning Performance Analysis, Applied Sciences, 11, 1085. DOI:10.3390/app11041805


Paskutinį kartą keista: ketvirtadienis, 2022 gruodžio 22, 13:21