In science education, learners are asked to have deep understanding and master scientific inquiry skills in science domain [33]. Thus, many kinds of scientific inquiry assessment [34, 101, 102] and learning activities [77] are used to assist learners in understanding the complex knowledge structure and varied process skills. In addition to traditional face-to-face learning activities, Hypermedia-based Learning Environments (HLE) are also used widely to support science education to enhance learning efficacy and balance teachers’ loading [58, 71]. The hypermedia-based learning environments are suitable to the science learning [51] because the free learning environments can provide non-linear learning processes, which can facilitate learners to construct knowledge structures on the basis of their own prior knowledge, and the diversified presentation are suitable to demonstrate varied process skills. However, without any support, most of the learners cannot obtain high learning performances due to the lack of self-regulated learning (SRL) abilities [36, 79], including planning goals, controlling strategies, monitoring performance, and reflecting on status [72]. SRL scaffoldings, which suggest or guide learners to regulate their learning when learners lack abilities to do the SRL well, are widely used to help learners learn in HLE [7].
SRL Scaffoldings are usually categorized into fixed scaffoldings, which are the same documents or suggestions for all learners, and adaptive scaffoldings, which can provide suggestions according to learners' learning status [5]. Azevedo and his colleague [6] apply fixed scaffoldings, adaptive scaffoldings, and no scaffoldings in learners' learning process to evaluate how various scaffoldings can affect learners' understanding of topics and SRL behaviors. In this research, the fixed scaffolding was a list of questions to remind learners to self-regulate their learning, and the adaptive scaffolding was suggestions provided by teachers
according to learners' status when learners cannot regulate their learning well. The experimental results of this research showed that adaptive scaffoldings based on ongoing learning diagnosis can significantly improve learners’ self-regulation and learning performances. However, in the real learning situation, it is costly for teachers to take care of all learners to provide adaptive scaffoldings. Thus, this dissertation aims to propose a Novel Adaptive Scaffolding Scheme, which can adopt teachers' expertise, to provide adaptive suggestions to help learners regulate their learning in the hypermedia-based learning environment. The main difficulty toward this goal is how to adopt teachers' expertise by using Information Technology (IT) to provide appropriate scaffoldings.
The mechanisms used in the IT domain to adopt teachers' knowledge in a system, called Intelligent Tutoring System (ITS), can be categorized into the conventional-program-based approach and the knowledge-based approach. The former uses hard-coded algorithm to simulate teachers’ teaching strategies. However, as an ITS is used wider and wider, more teaching strategies are required to be applied to satisfy various learners' needs. The teaching strategies embedded in the algorithms are difficult to be maintained and acquired, so the cost of refining and maintaining algorithm would grow rapidly. The latter separates the domain expertise, represented by knowledge models, from the inference logics, so the teachers' knowledge can be refined easily without changing program codes. The knowledge model, explicitly representing teachers’ teaching strategies, can also facilitate to maintain and acquire teachers' knowledge. However, the variety of learners' portfolios and requirements in the non-linear learning processes of HLE makes the adopted teaching strategies complex, so the major challenge to apply knowledge-based approaches is how to design a suitable knowledge model to satisfy the teaching requirements of the non-linear learning processes provided by the free HLE. This dissertation defines and solves three subproblems caused by providing
adaptive scaffoldings in the non-linear learning processes, where three knowledge models are proposed in the Novel Adaptive Scaffolding Scheme to solve the three subproblems, respectively. The subprobems, the related ITS mechanisms, and the proposed ideas are listed as follows.
Learning Process Representation Subproblem
The learning process in HLE is non-linear, which usually makes learners difficult to plan their learning goals, because these learners cannot choose the most suitable learning paths to their learning status among the large amount of choices. Thus, in order to suggest learners appropriate learning paths, teachers' typical learning paths for various kinds of learners and rules of selecting learning paths need to be adopted in the adaptive scaffolding scheme.
Existing ITS mechanisms which provide adaptive navigation support [2, 14, 18, 20, 44, 78, 91] can facilitate teachers to generate typical learning plans and suggest learners with the appropriate learning paths. However, for the non-linear learning processes, the typical learning plans need to be complex with many candidate learning paths, and this kind of mechanisms still lack the knowledge model which can fulfill both expressive power and understandability. A Learning Process Representation Subproblem occurs where a knowledge model needs to be designed to satisfy both adequate expressive power to represent teachers' learning path selection knowledge and good understandability for teachers to provide their expertise.
In order to solve the Learning Process Representation Subproblem, the proposed scheme generalize a finite state machine, which is an easy-to-understand model to represent conditional processes, to represent the learning processes. The new model proposed in this
dissertation is named Generalized Finite State Machine, where the states denote learning activities and the extended transition functions express the complex teaching strategies of learning path selection.
Personalized Content Adaptation Subproblem
In the non-linear learning processes in the free HLE, learners have diversified learning progress and even various learning environments and meida. Choosing appropriate content to satisfy learning requirements is a key task of controlling learning for a learner. However, the diverse requirements need to be satisfied by providing huge number of versions for each content, and it is difficult for learners to find appropriate versions by themselves.
Learning recommender mechanisms in ITS [29, 46, 54, 65, 66] can recommend existing learning materials to learners according to learning styles, prior knowledge, and environments, but the huge number of content is needed for the learners' diversified needs. Content Adaptation mechanisms [11, 15, 30, 53, 57, 75, 97, 99] can dymanically generate new content for learners' requirements by fragmenting and recombining original content. However, managing large number of content fragments to efficiently provide learners appropriate content is still difficult, which is called a Personalized Content Adaptation Subproblem.
In order to solve the Personalized Content Adaptation Subproblem, the original content is decomposed into blocks with the inner media and text. Various versions of content can be generated for various requirements by transcoding some media. A Multi-Granularity Content model is proposed to manage these versions of content, where a version is represented as three granularities: page level, block level, and media level. When a request is received, the system can retrieve the most suitable version of the page firstly to find the more
or less suitable content version and adapt it to the detailed requirements by replacing the blocks from other versions and transcoding the media if the request time is acceptable. Thus, the content adaptation process from coarse-grained to fine-grained can efficiently retrieve good-enough content and effectively refine it to the suitable content.
Process Skill Diagnosis Subproblem
In addition to the traditional lectures and examinations, an HLE can provide varied learning media and activities, such as virtual laboratories, to enhance the effectiveness of science learning. These activities can generate various learner portfolios to record learners' behavior and performances for monitoring and reflecting on their learning status. However, the various learning paths and diverse learner portfolios in the non-linear learning processes make moitoring and reflecting difficult because learners are difficult to refer to peers' performances and progress.
Existing learning diagnosis mechanisms [17, 38, 45, 56, 62] in ITS domain can effectively diagnose learners' performance by traditional assessment results, but the ideas of these mechanisms are difficult to be applied to diagnose scientific process skills and learning behaviors for the science learning because heterogenerous learner portfolios generated by the scientific learning activities cannot be analyzed by the existing appraoches. Thus, a Process Skill Diagnosis Subproblem is defined as how to manage and organize the heterogenerous learner portfolios and provide learning diagnosis by applying the teaching expertise to these diversified portfolios to assist learners in monitoring and reflecting on learning status.
For the Process Skill Diagnosis Subproblem, because the heterogeneous learner portfolios are difficult to be analyzed and used in learning diagnosis, a Heterogeneous
Knowledge Diagnosis model is proposed where the learners' behaviors in science learning activities can be identified as correct or incorrect actions by using the proposed key operation action patterns. These heterogeneous learning events are organized into an ontology-based knowledge structure, which is a tree of concepts and process skills with the relations connecting among the nodes. With the relations of concepts and process skills, it is easy to find the causal relationships between events and learning performances. Thus, the high-level learning diagnosis knowledge is easy to be applied to the organized portfolios.
Generally speaking, this dissertation proposes a novel adaptive scaffolding scheme to assist learners in self-regulating their learning in the scientific learning domain. This knowledge-based scheme applies teachers' educational knowledge to diagnose learners' learning status and provide scaffoldings to fit individual learners' needs. In order to evaluate the effectiveness of the proposed novel adaptive scaffolding scheme, three sub-systems based upon the three proposed models, including a Generalized Finite State Machine, a Multi-Granularity Content model, and a Heterogeneous Knowledge Diagnosis model, were constructed and the corresponding experiments were conducted. The results show that the adaptive scaffoldings for planning learning based on Generalized Finite State Machine can significantly improve low-grade learners' learning performances. For supporting learning content selection and adaptation, the proposed Multi-Granularity Content model is more efficient than the previous approaches to provide more appropriate content. For monitoring and reflecting on learners learning status, the scaffoldings based on the Heterogeneous Knowledge Diagnosis model can also improve learners' motivation to understand learning problems.
In the rest of the dissertation, the related works about Self Regulated Learning
scaffolding systems and the issues raised for non-linear scientific learning processes are introduced in Chapter 2. In order to overcome these issues, a Novel Adaptive Scaffolding scheme is proposed and described in Chapter 3, where three subproblems about non-linear scientific learning processes and the corresponding models used to solve the problems are introduced. In Chapters 4, 5, and 6, the three models and their evaluation are detailedly described, respectively. Afterward, a conclusion and the references used in this dissertation are provided in Chapters 7 and 8, respectively. Finally, the cases of applying the proposed models to real learning situations are given in appendices.