自我組織式行為塑模應用於程式學習之研究

Abstract

資訊科學與工程領域的學生在學習程式語言的過程中，問題解決能力的訓練一直是很重要的課題。給一個問題時，學生要可以分析問題的屬性，判斷並選擇最適當的問題解決策略來設計程式演算法並實作。問題解決的教學一般來說是不容易的，因為不同學生即使在學習同一個問題解決策略時，常會有不同的思考錯誤徵狀發生，而不同的徵狀背後也往往是由於不同的邏輯錯誤或迷失概念造成的，也因此造成學習診斷上的困難。為了能確切診斷並提供適性化的學習導引，因此需要定義出學習行為模型來描述學習狀態。隨著Web 2.0式數位學習的蓬勃發展，學生的學習歷程，有機會透過社群網絡網站、討論版網站、社群標籤分享網站、維基百科與線上遊戲等平台，能更完整的紀錄下來以提供更精確的行為模式之分析，然而Web 2.0平台上相較於傳統課堂上單純只有老師與同學的學習環境，由於學習情境的多變性(如:社交關係)，因此不容易透過靜態分析就能一次預定出學生完整的行為模型。因此Web 2.0式數位學習的學習行為模型的設計有三個技術上的問題，分別是如何提供延伸性以反映行為模式的變化、如何保有穩定性以避免受到雜訊影響分析、如何保有可讀性來提供學習評量上的使用。為了解決上述這些問題，在本論文中使用知識工程技術，提出了使用本體論來定義後設資料註記之結構的方式，來標註記錄下來的學習歷程。將學生的行為模型透過本體論的概念與結構來敘述，因此可以將動態環境中學習模型之設計問題，轉成本體論建構與維護之問題。因此提出了知識本體論結晶化的概念來塑模學習行為。透過素民式知識擷取與歷程資料探勘來延伸本體論，透過群體驗證與後設資料驗證來確保本體論的穩定性，透過本體論之邏輯結構定義，搭配規則式評量系統來提供行為模型的可讀性。在實驗驗證方面，實際應用在大專生遞迴解題策略之線上學習平台上，以及國中生布林邏輯的學習上，實驗結果發現透過行為塑模提供的學習導引，能有效的提升學生的學習效果。

The problem solving capability is important for students in the Programming course of Computer Science. While given the problem, students need to firstly analyze the problem and select an appropriate problem solving strategy for further flow chart design and code implementation. Problem solving in programming is generally considered to be difficult because different students usually have different error symptoms and the symptoms usually have different root causes. Therefore, to provide the adaptive learning guidance, a behavior model is needed to describe the students’ learning status. In Web 2.0-based e-Learning environment, the students’ portfolio on the platforms of social network service, web forum, social bookmark, Wikipedia, web games, etc. can be used as the potential resources to build the students’ behavior model precisely. However, the changing of learning context such as social networks on the Web 2.0 platforms make it more difficult to predefine the students’ behavior model using one shot approach. Therefore in Web 2.0-based e-Learning, there are three technical issues for behavior modeling which are the extensibility for modeling the evolving behaviors, the stability for noise handling, and the understandability for learning behavior assessment. How to provide a learning behavior model which can be self-organized to maintain and discover the evolving behaviors becomes an important and challenging issue. In this dissertation, the learning behavior modeling problem is defined as that given the learning content and context of learning activity, how to model the students’ behaviors to provide adaptive learning guidance. Under different learning contexts, the behavior modeling problem can be reduced to the problem solving strategy formulation and realization problem for self programming learning, the trustworthy experts modeling problem for inquiry learning, and the consensus building problem for folksonomy-based knowledge sharing activity. With our observations, students could have meaningful learning actions when the purposes of the actions in specific learning context are obtained. Accordingly, to build the students’ behavior model, the Purpose-based Ontology is built to model the purpose of the actions. Next, the ontology-based learner behavior modeling approach is proposed to analyze the frequent action patterns and organize the obtained patterns with the structure of ontology as learning behaviors. Therefore, the learning guidance issues under different contexts can be resolved by the following behavior modeling approaches to provide the adaptive learning guidance based on the built behaviors. Under the context of intelligent tutoring system, the Generalized Model Tracing approach is proposed to organize the diagnosis results of different program model tracing with Problem Solving Strategy Ontology. The diagnosis result can be used to provide the learning guidance for problem solving strategy. Under the context of learning forum, the Cascading Topic Clustering Algorithm and Self-organized Ontology Maintenance Scheme are proposed to organize the forum experts’ inquiry activities with the Purpose-based Ontology. Building the forum experts’ behavior model can provide trustworthy expert finding service for inquiry-based learning. Under the context of collaborative constructed sharable content repository, the IRT-Based Metadata Reengineering Scheme is proposed to evaluate the effectiveness of folksonomy tags and resolve the synonym, redundancy and incompleteness problem of metadata by the domain taxonomy. Accordingly, the tag effectiveness value can detect the conflict and provide the tagging guidance to resolve the consensus building problem to obtain the well-tagged metadata. To evaluate the proposed behavior modeling approach, the applications with different learning contexts are investigated including adaptive programming misconception diagnosis, game rule tuning learning activity, trustworthy expert finding service for inquiry learning on the programming learning forum, and intelligent solution retrieval system. The experiments for students’ learning effectiveness have been done. The experimental results show the applications with behavior models have higher learning effects.