学习分析系统(LAS)具有把不同的资源和服务整合来为教育提供最好的实践.学习分析引擎作为这一系统的核心组成部分, 目前在功能上的限制,定义上的模糊, 以及可扩展性差使其不能扩展到其他内容和机构.本文首先提出了引擎本体(角色、来源、时间和控制)来描述和独特的四种引擎功能:预测、反思、建议和适应, 以建立一个共同的语言和实践学习分析引擎,从而改善不同的\,LA\,之间的互操作性应用. 基于这些本体论的引擎,本研究进一步设计LAS引擎的机制和应用数学模型来解释其分解和重组技术.本文所阐述的学习分析系统引擎, 预计可用于开发一个开放和集成的,能够扩大规模和扩展到任何环境中的LAS.

Learning analytics system (LAS) has the potential to pull together diverse resources and services to leverage the best practices for education. As the central component of this system, current LAS engines have been limited in function and vaguely defined as well as poor scalability and extensibility to other
contexts and institutions. This paper first proposed engine ontology, role, source, time and control, to describe and distinct four engine functions: Prediction, Reflection, Recommendation, and Adaptation, in order to establish a common language and practice for LA engines, and in turn improve interoperability between different LA applications. Based on those ontological engines, this study further designed a mechanism of LAS engines and applied mathematical
modeling to explain its decomposition and recombination techniques. This LAS engines is expected to power an open and integrated LAS that is capable of scaling up and extensible to any context.