Huang Ronghuai

Frame-Based Calculus of Solving Arithmetic Multi-Step Addition and Subtraction Word Problems

Computer-assisted instruction systems have been broadly applied to help students solving math word problems. However, these systems offer little help to students encountering multi-step arithmetic word problems that do not be stored in the predesigned database. MSWPAS is a computer simulation system of people’s arithmetic multi-step addition and subtraction word problems behavior, which composed of MSWPAS-NP and MSWPAS-CP. In MSMPAS-NP, the natural language of word problems are processed and reflected into frames, and in MSMPAS-CP, calculus is performed based on those frames. This paper focuses on MSMPAS-CP. Psychological theoretical foundation and knowledge representation of MSMPAS-CP are presented at first. A method of frame-based calculus for solving multi-step addition and subtraction word problems is proposed, and then the architecture of system and how it works are discussed. Finally, an experiment is carried out, which shows that the method of frame-based calculus can effectively solve multi-step word problems.

Investigation of Key School-related Indicators Influencing ICT in K-12 Education

In the first phase, Delphi technique was conducted to identify key indicators influencing promotion ICT in K-12 schools settings. The result of the qualitative phase revealed that ICT leadership development, teacher professional development, optimized learning spaces, richness of digital resources, innovative ways of teaching and learning, changes in learning assessment forms six key indicators for ICT in K-12 education. At the second phase of the research, cases from China were selected and scored in each six indicators, and path modeling was built. The findings of this research provide important implications for the evaluation and improvement of the process of ICT reshaping K-12 Education.

SMID: A Semantic Model of Instructional Design

This paper proposes a general method on how to build a semantic model for instructional design. At the meantime, a semantic model for instructional design is also proposed (called SMID), which mainly includes ontology and processing module. SMID ontology includes several ontologies used in instructional design, such as knowledge object ontology, material object ontology, learning goal ontology, instructional strategy ontology, learning action ontology; SMID processing module means the processing method used in instructional design, including knowledge selection, instructional strategy selection, learning action sequence generation, learning resource generation. With J2EE Platform and Drool tools, the SMID system is implemented, and some experiments with real course are done and conclusions are also given.

Ontology-Based Learning Resource Dynamic Generation with Learning Goal

In this paper, based on ontology, a kind of system architecture is proposed to generate learning resource dynamically with learning goal, which is called LRDG. At the meantime, a semantic model and related processing methods are designed. The semantic model consists of knowledge object ontology, learning material ontology, learning action ontology and instructional strategy rule format. Processing methods include knowledge object decomposition, knowledge object selection, rule reasoning engine, and learning resource generation. With LRDG, the learning resource can be generated at learning time according to learner¿s present knowledge state. Also, a system is implemented with J2EE platform and Drool rule engine, and a prototype course is developed. At last, the author gives experiment result and draws conclusion.In this paper, based on ontology, a kind of system architecture is proposed to generate learning resource dynamically with learning goal, which is called LRDG. At the meantime, a semantic model and related processing methods are designed. The semantic model consists of knowledge object ontology, learning material ontology, learning action ontology and instructional strategy rule format. Processing methods include knowledge object decomposition, knowledge object selection, rule reasoning engine, and learning resource generation. With LRDG, the learning resource can be generated at learning time according to learner’s present knowledge state. Also, a system is implemented with J2EE platform and Drool rule engine, and a prototype course is developed. At last, the author gives experiment result and draws conclusion. KeywordsOntology; Semantic Model; Instructional Design; Learning Goal; Learning Resourc Dynamic Generation