What if learning analytics were based on learning science?

Zahia Marzouk

doi:10.14742/ajet.3058

What if learning analytics were based on learning science?

2016-12-15

64

OA

AI

DOI：10.14742/ajet.3058

原文链接

原文求助

摘要

En 中文

Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students' decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning science that explain how students learn. We present six learning analytics that reflect what is known in six areas (we call them cases) of theory and research findings in the learning sciences: setting goals and monitoring progress, distributed practice, retrieval practice, prior knowledge for reading, comparative evaluation of writing, and collaborative learning. Our designs demonstrate learning analytics can be grounded in research on self-regulated learning and self-determination. We propose designs for learning analytics in general should guide students toward more effective self-regulated learning and promote motivation through perceptions of autonomy, competence, and relatedness.

Keyword:

SELF-DETERMINATION THEORY

MOTIVATION

KNOWLEDGE

COMPREHENSION

ENGAGEMENT

PRINCIPLES

RETRIEVAL

RECALL

MEMORY

TESTS

What if learning analytics were based on learning science?

摘要

期刊

机构

引用论文