輔助超媒體科學學習環境中自我調適學習之適性化鷹架系統

Abstract

科學教育的目標是要建立學習者的科學知識架構與各種科學過程技能，而不同背景知識、不同學習風格的學習者常常需要不同的學習路徑來了解目標的知識與技能，超媒體學習環境能夠提供科學學習較好的學習成效，因為這種有彈性的學習環境能夠提供非線性的學習流程以符合不同的學習需求，學習者能夠選擇自己適合的路徑學習來學習目標概念，而多樣化的表現媒體也更適合展示各種過程技能的教學。然而，因為學習者缺乏自我調適學習的能力來決定自己的學習路徑與策略，非線性學習流程中大量的學習流程選擇也造成學習上的困難。 因此學習鷹架常常被使用來幫助低自我調適學習能力的學習者當這些學習者在學習中不知如何調適自己的學習。適性學習鷹架是不斷的觀察學生的學習狀況，並適時適性的給予學習輔助的方法，根據之前的研究，適性學習鷹架比固定型的學習鷹架更能增進學習成效，但是，提供適性學習鷹架將會造成老師沉重的教學負荷。雖然一些智慧型家教系統也能夠模擬老師的教學策略來提供適性學習鷹架自動幫助學習者，但要在非線性學習流程中使用這些既有的方法卻仍舊有困難，因為分歧的學習歷程與背景知識使得智慧型家教系統所要實作的老師教學策略必須比以往的線性流程還要複雜許多。 也因此產生了三個針對非線性學習流程建立學習鷹架而造成的子問題：描述非線性學習計畫、為多樣的學習者需求調適學習內容、由異質性的學習歷程診斷學習狀況。這篇論文提出一個新的適性化鷹架系統，其中擴展的狀態機模型、多顆粒粗細度的學習內容模型、以及以本體論為基礎的知識架構被設計來分別解決上述的三個子問題，論文中也提供了相對應的驗證結果與應用的實際案例。

Science education aims to build learners' scientific knowledge structure and varied process skills. The scientific learners who have various prior knowledge and learning styles usually need various learning processes to master the concepts or skills. This learning requirement can be fulfilled by Hypermedia-based Learning Environments, where the free learning environments can provide a non-linear learning process for various learning needs. In the non-linear learning process, learners can freely select appropriate learning paths to achieve the learning goal and the diversified presentation can demonstrate varied process skills. However, the large number of learning choices provided by this kind of flexible learning environment usually make learning more difficult if learners lack self-regulated learning (SRL) abilities to decide their learning processes and strategies. Thus, scaffoldings, which suggest or guide learners when learners cannot self-regulate their learning, are usually used to help low-SRL-ability learners. According to previous researches, adaptive scaffoldings, which dynamically provide learners assistance according to learners' status, can improve learning performance and facilitate SRL behaviors better than fixed ones, but providing adaptive scaffoldings would cause heavy loads on teachers. Although some of existing Intelligent Tutoring System (ITS) approaches can provide adaptive scaffoldings, applying these approaches in the non-linear learning processes is still difficult. This is because the diverse portfolios and prior knowledge generated by various processes cause the teaching strategies more complex than ones for linear learning processes. Thus, In this dissertation, three subproblems about representing non-linear learning plans, adapting learning content to diverse learners' requirements, and diagnosing learners' status by heterogeneous portfolios are defined. For solving these subproblems, a novel adaptive scaffolding scheme is proposed, where a generalized finite state machine, a multi-granularity learning content model, and an ontology-based knowledge structure are designed to solve the three subproblems, respectively. The evaluation results and the applying cases are also provided in this dissertation.