作 为 学 生 自 我 导 向 学 习 的 引 导 工 具。学 习 分 析 技 术为 学 生 打 开 了 一 个 与 学 习 内 容、同 伴 和 教 师 交 互 的 窗 口，提供 了 促 进 自 我 导 向 的 学 习 的 机 会。自 我 监 控 对 学 习 者 的 自 我导 向 学 习 是 很 重 要 的，通 过 学 习 分 析 技 术，学 生 可 以 分 析 自己 的 学 习 过 程 数 据，通 过 可 视 化 结 果 回 顾 学 习 的 时 间、时 长和 内 容，并 通 过 与 同 伴 的 比 较 了 解 自 己 的 进 度[46]。

This alliance would also strengthen our opportunities to influence non-academic research and practice.

This difference is associated with another difference between the two communities: the type of adaptation and personalization typically supported by the two communities. In line with the greater focus on automated discovery in EDM, EDM models are more often used as the basis of automated adaptation, conducted by a computer system such as an intelligent tutoring system. By contrast,

As schools, university, and corporate learning and curriculum organizations begin to adopt data mining and analytics, both LAK and EDM can benefit from building off work occurring in the other community.

Higher education has been accused of a “one size fits all” approach

Modern education institutions are required to balance their role as a public good alongside the need to remain financially viable–if not profitable. Early work in learning analytics wasseen to provide solutions to balance this agenda. Increased student diversitya