Consideration factors and adoption of type,tabulation and framework for creating e-portfolios

The purpose of this research is to analyze the content of e-portfolios created by students in order to understand their tabulation and ways of displaying content. The analytic result shows that the number of outcome portfolios created by students is more than that of process portfolios. The five types of e-portfolio tabulation, in order of those most commonly created by students, are combination-based, content item-based, work-based, course unit-based, and time-based. The combination-based type incorporates the advantages of other tabulation types, while the content item-based and work-based types are better for clearly classifying data and step-by-step organization of it. Future research may further explore factors related to students’ decision of tabulation type, the difficulties they face in the process, and their mentality as they adopt a portfolio type.     

Boosted multi-task learning

In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between ? ₁-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.     

A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners?? preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners?? requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.     

New Compliance-Mechanism Design for Small Companion Robots

The compliance mechanisms used on robotic arms can be classified into two major categories: mechanical and electronic. The ideal characteristics of a compliance mechanism include small volume, simple mechanical structure, low cost, large complaint range, and high precision and accuracy under displacement control. Most mechanical compliance mechanisms are able to meet the first three conditions but have a small compliant range and low precision and accuracy under displacement control. The electronic compliance mechanism is hardly limited in the degree of deformation and comes with a higher precision and accuracy under the displacement control, but its sensors are expensive and the system is difficult to control. To combine the advantages of both types, this research aims to develop a new design of compliance mechanism in which a small-scale torque-limiting mechanism with a self-locking feature is installed between the actuator and the arm structure to minimize the volume while providing an ample torque limit. When the robotic arm is overloaded under an external force, a slide will occur inside the compliance mechanism so that the robotic arm will move along the direction of the external force to avoid damage. The robotic arm will automatically return to its original position after the external force is removed. The new compliance mechanism not only exceeds most of the current mechanical designs in the range of compliance but also does not affect the precision and accuracy of the displacement control. Furthermore, the new compliance mechanism does not require any sensors, which will benefit small robotic arms.     

An auto-scoring mechanism for evaluating problem-solving ability in a web-based learning environment

The rapid development of computer and network technologies has attracted researchers to investigate strategies for and the effects of applying information technologies in learning activities; simultaneously, learning environments have been developed to record the learning portfolios of students seeking web information for problem-solving. Although previous research has demonstrated the benefits of applying information technologies to learning activities, the difficulties in doing so have also been revealed. One of the major difficulties is the lack of a mechanism to assist teachers in evaluating the problem-solving ability of the students, such that constructive suggestions can be given to the students, and tutoring strategies can be improved accordingly.     

Mining demand chain knowledge of life insurance market for new product development

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.     

Data mining for adaptive learning sequence in English language instruction

The purpose of this paper is to propose an adaptive system analysis for optimizing learning sequences. The analysis employs a decision tree algorithm, based on students’ profiles, to discover the most adaptive learning sequences for a particular teaching content. The profiles were created on the basis of pretesting and posttesting, and from a set of five student characteristics: gender, personality type, cognitive style, learning style, and the students’ grades from the previous semester. This paper address the problem of adhering to a fixed learning sequence in the traditional method of teaching English, and recommend a rule for setting up an optimal learning sequence for facilitating students’ learning processes and for maximizing their learning outcome. By using the technique proposed in this paper, teachers will be able both to lower the cost of teaching and to achieve an optimally adaptive learning sequence for students. The results show that the power of the adaptive learning sequence lies in the way it takes into account students’ personal characteristics and performance; for this reason, it constitutes an important innovation in the field of Teaching English as a Second Language (TESL).     

Effective temporal data classification by integrating sequential pattern mining and probabilistic induction

Data classification is an important topic in the field of data mining due to its wide applications. A number of related methods have been proposed based on the well-known learning models such as decision tree or neural network. Although data classification was widely discussed, relatively few studies explored the topic of temporal data classification. Most of the existing researches focused on improving the accuracy of classification by using statistical models, neural network, or distance-based methods. However, they cannot interpret the results of classification to users. In many research cases, such as gene expression of microarray, users prefer the classification information above a classifier only with a high accuracy. In this paper, we propose a novel pattern-based data mining method, namely classify-by-sequence (CBS), for classifying large temporal datasets. The main methodology behind the CBS is integrating sequential pattern mining with probabilistic induction. The CBS has the merit of simplicity in implementation and its pattern-based architecture can supply clear classification information to users. Through experimental evaluation, the CBS was shown to deliver classification results with high accuracy under two real time series datasets. In addition, we designed a simulator to evaluate the performance of CBS under datasets with different characteristics. The experimental results show that CBS can discover the hidden patterns and classify data effectively by utilizing the mined sequential patterns.     

Discriminating between disturbance and process model mismatch in model predictive control

A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process.