Appropriate media choice for e-learning effectiveness: Role of learning domain and learning style

As the number of online education and training programs increase, researchers and practitioners are interested in investigating ways to design and develop effective e-learning programs. One of the major design decisions that affects learning effectiveness is the choice of media to present the contents of such programs. The prevailing tendency seems to be to use “richer” medium, in the progression from text to graphics to audio to video, for designing and developing e-learning programs. It is not clear, however, if a “richer” medium provides proportionately higher learning effectiveness. To investigate this gap in our understanding, we developed an integrated research model and tested it empirically. Our results showed that the relationship between media choice in an e-learning program and the effectiveness of that program is moderated by the learning domain of the program and the learning styles of learners.     

Evaluating pedagogical classification frameworks for learning objects: A case study

The use of toolkits and reference frameworks for the design and evaluation of learning activities enables the systematic application of pedagogical criteria in the elaboration of learning resources and learning designs. Pedagogical classification as described in such frameworks is a major criterion for the retrieval of learning objects, since it serves to partition the space of available learning resources depending either on the pedagogical standpoint that was used to create them, or on the interpreted pedagogical orientation of their constituent learning contents and activities. However, pedagogical classification systems need to be evaluated to assess their quality with regards to providing a degree of inter-subjective agreement on the meaning of the classification dimensions they provide. Without such evaluation, classification metadata, which is typically provided by a variety of contributors, is at risk of being fuzzy in reflecting the actual pedagogical orientations, thus hampering the effective retrieval of resources. This paper describes a case study that evaluates the general pedagogical dimensions proposed by Conole et al. to classify learning resources. Rater agreement techniques are used for the assessment, which is proposed as a general technique for the evaluation of such kind of classification schemas. The case study evaluates the degree of coherence of the pedagogical dimensions proposed by Conole et al. as an objective instrument to classify pedagogical resources. In addition, the technical details on how to integrate such classifications in learning object metadata are provided.     

Layered learning design: Towards an integration of learning design and learning object perspectives

The use of ICT to enhance teaching and learning depends on effective design, which operates at many levels of granularity from the small to the very large. This reflects the range of educational problems from course design down to the design of activities focused on specific learning objectives. For maximum impact these layers of design need to be co-ordinated effectively. This paper delineates a reference model of ‘layered learning design’ where designs at one layer should use and incorporate designs from lower (more specific) layers in elegant and powerful ways. This would allow different designers, or tutors, to focus on different levels of abstraction in the learning design process, and to collaborate in combining designs to make a substantial impact on practice.     

Evaluating the use of learning objects in Australian and New Zealand schools

The Le@rning Federation, an agency funded by Australian and New Zealand governments, initiated a Field Review project as the start of a long-term research study to evaluate the impact, application and effectiveness of the online digital content developed according to the learning object model. In terms of content, the pilot Field Review found that many learning objects provided stimulating and diverse learning experiences for students. It is one of the potential strengths of learning objects that they are able to provide new geographical experiences and simulate dangerous or expensive learning activities at low relative cost.     

A fully personalization strategy of E-learning scenarios

The personalization in E-learning systems has been the subject of many recent research efforts. While a large number of systems have been implemented, many of these systems allow the application of very few if not just one predefined personalization strategy. This is a constraint for providing effective E-learning experience and for rationalizing the personalization needs of the pedagogues, the professors and the learners. In this paper, we propose a new approach for personalization of learning scenarios based on two levels: The first level allows the personalization of learning scenarios according to a predefined personalization strategy. The second level allows teachers to select personalization parameters and combine them flexibly to define different personalization strategies according to the specifics of courses. The proposed solution is a step to federate the research efforts on the E-learning personalization by integrating and combining the personalization parameters. Concerning the technological aspect, Web service technology constitutes an operational solution for implementing our approach and for the interoperability with other E-learning personalization systems. Beside the implementation of an interoperable solution, we also aim to enable teachers to provide proper personalized learning scenarios.     

A learning style classification mechanism for e-learning

With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student’s learning style. Hence, the first step for achieving adaptive learning environments is to identify students’ learning styles. This paper proposes a learning style classification mechanism to classify and then identify students’ learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students’ learning styles.     

学习指导的设计与实现

针对远程教育平台中,因缺乏有效的学习控制、个性化的引导、调节等而导致平均学习效率低下的问题,提出了一种基于知识点架构的个性化课程导学模型.该模型根据课程中知识点的本体结构、资源档案以及学习者的档案和学习行为等,通过知识点本体结构与图的转换、路径权值更新、路衽搜索、内容筛选、本体更新等步骤,实时地定制出适合于学习者个体的学习内容.在实验中,方法应用于上海交通大学网络教育学院的<数据结构>课程.经分析与研究说明,算法不仅能够帮助学习者完成课程的学习,并能有效地控制、引导学习者对资源的访问和课程的学习,达到了提高学习者学习质量和学习兴趣的日的;同时,该方法也能够发现课程初始本体结构中不合理的关系.     

Experience beyond knowledge: Pragmatic e-learning systems design with learning experience

With the growing demand in e-learning system, traditional e-learning systems have dramatically evolved to provide more adaptive ways of learning, in terms of learning objectives, courses, individual learning processes, and so on. This paper reports on differences in learning experience from the learner’s perspectives when using an adaptive e-learning system, where the learner’s knowledge or skill level is used to configure the learning path. Central to this study is the evaluation of a dynamic content sequencing system (DCSS), with empirical outcomes being interpreted using Csikszentmihalyi’s flow theory (i.e., Flow, Boredom, and Anxiety). A total of 80 participants carried out a one-way between-subject study controlled by the type of e-learning system (i.e., the DCSS vs. the non-DCSS). The results indicated that the lower or medium achievers gained certain benefits from the DCSS, whilst the high achievers in learning performance might suffer from boredom when using the DCSS. These contrasting findings can be suggested as a pragmatic design guideline for developing more engaging computer-based learning systems for unsupervised learning situations.     

ALOCOM: a generic content model for learning objects

e-Learning organizations are focusing heavily on learning content reusability. The ultimate objective is a learning object economy characterized by searchable digital libraries of reusable learning objects that can be exchanged and reused across various learning systems. To enable such approach, basic questions of learning content interoperability need to be addressed. This paper investigates the interoperation of learning content defined according to different specifications. A number of content models are reviewed that define learning objects and their components. On the basis of a comparative analysis, the content models are mapped to a generic model for learning objects to address interoperability questions and to enable share and reuse on a global scale.     

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.     

A qualitative evaluation of evolution of a learning analytics tool

LOCO-Analyst is a learning analytics tool we developed to provide educators with feedback on students learning activities and performance. Evaluation of the first version of the tool led to the enhancement of the tool’s data visualization, user interface, and supported feedback types. The second evaluation of the improved tool allowed us to see how the improvements affected the users’ perceived value of the tool. Here, we present the qualitative results of our two evaluations and discuss important lessons learned stemming from the comparison of the two studies. The results show that educators find the kinds of feedback implemented in the tool informative and they value the mix of textual and graphical representations of different kinds of feedback provided by the tool.     

SemDM: Task-oriented masking strategy for self-supervised visual learning

In this paper, we propose a novel learning scheme for better combining masked image modeling (MIM) and instance discrimination (ID). Motivated by compensating the requirement gap of masking strength between MIM and ID, we propose Semantic Disjoint Masking (SemDM), which decomposes the masking into two manners: preserving the majority of key patterns in images for ID, while dropping out most of them for MIM. Specifically, we utilize attention-guided masking in ID to help keeping the identity of object in image for encoder. While in MIM, we conversely only leave some hints about the object. Then these generated masked views only perform their specified learning task, facilitating more suitable visual priors to be learned in each learning task. Moreover, we introduce product quantization (PQ) to optimize the concept distributions in latent space, which guarantees that a compact set of meaningful visual concepts can be learned. Extensive experiments demonstrate that our method bootstraps meaningful visual concepts to guide visual understanding, and obtains state-of-the-art results on ImageNet-100.     

A Pareto biogeography-based optimisation for multi-objective two-sided assembly line sequencing problems with a learning effect

This research presents a Pareto biogeography-based optimisation (BBO) approach to mixed-model sequencing problems on a two-sided assembly line where a learning effect is also taken into consideration. Three objectives which typically conflict with each other are optimised simultaneously comprising minimising the variance of production rate, minimising the total utility work and minimising the total sequence-dependent setup time. In order to enhance the exploration and exploitation capabilities of the algorithm, an adaptive mechanism is embedded into the structure of the original BBO, called the adaptive BBO algorithm (A-BBO). A-BBO monitors a progressive convergence metric in every certain generation and then based on this data it will decide whether to adjust its adaptive parameters to be used in the next certain generations or not. The results demonstrate that A-BBO outperforms all comparative algorithms in terms of solution quality with indifferent solution diversification.     

Integrating learning styles and adaptive e-learning system: Current developments,problems and opportunities

Learning styles which refer to students’ preferred ways to learn can play an important role in adaptive e-learning systems. With the knowledge of different styles, the system can offer valuable advice and instructions to students and teachers to optimise students’ learning process. Moreover, e-leaning system which allows computerised and statistical algorithms opens the opportunity to overcome drawbacks of the traditional detection method that uses mainly questionnaire. These appealing reasons have led to a growing number of researches looking into the integration of learning styles and adaptive learning system. This paper, by reviewing 51 studies, delves deeply into different parts of the integration process. It captures a variety of aspects from learning styles theories selection in e-learning environment, online learning styles predictors, automatic learning styles classification to numerous learning styles applications. The results offer insights into different developments, achievements and open problems in the field. Based on these findings, the paper also provides discussion, recommendations and guidelines for future researches.     

Evaluation and selection of group recommendation strategies for collaborative searching of learning objects

Nowadays, there is a wide variety of e-learning repositories that provide digital resources for education in the form of learning objects. Some of these systems provide recommender systems in order to help users in the search for and selection of the learning objects most appropriate to their individual needs. The search for and recommendation of learning objects are usually viewed as a solitary and individual task. However, a collaborative search can be more effective than an individual search in some situations – for example, when developing a digital course between a group of instructors. The problem of recommending learning objects to a group of users or instructors is much more difficult than the traditional problem of recommending to only one individual. To resolve this problem, this paper proposes a collaborative methodology for searching, selecting, rating and recommending learning objects. Additionally, voting aggregation strategies and meta-learning techniques are used in order to automatically obtain the final ratings without having to reach a consensus between all the instructors. A functional model has been implemented within the DELPHOS hybrid recommender system. Finally, various experiments have been carried out using 50 different groups in order to validate the proposed learning object group recommendation approach.     

A unit test approach for database schema evolution

Context

The constant changes in today’s business requirements demand continuous database revisions. Hence, database structures, not unlike software applications, deteriorate during their lifespan and thus require refactoring in order to achieve a longer life span. Although unit tests support changes to application programs and refactoring, there is currently a lack of testing strategies for database schema evolution.

Objective

This work examines the challenges for database schema evolution and explores the possibility of using various testing strategies to assist with schema evolution. Specifically, the work proposes a novel unit test approach for the application code that accesses databases with the objective of proactively evaluating the code against the altered database.

Method

The approach was validated through the implementation of a testing framework in conjunction with a sample application and a relatively simple database schema. Although the database schema in this study was simple, it was nevertheless able to demonstrate the advantages of the proposed approach.

Results

After changes in the database schema, the proposed approach found all SELECT statements as well as the majority of other statements requiring modifications in the application code. Due to its efficiency with SELECT statements, the proposed approach is expected to be more successful with database warehouse applications where SELECT statements are dominant.

Conclusion

The unit test approach that accesses databases has proven to be successful in evaluating the application code against the evolved database. In particular, the approach is simple and straightforward to implement, which makes it easily adoptable in practice.     

面向数据共享目的的GIS语义数据模型

提出了一种ＧＩＳ语义数据模型,试图通过该模型从技术上解决（至少是部分地解决）部门之间的数据共享问题。给出了该模型的一化框架,及有关概念严格的,形式化的定义,并给出了一个应用实例,最后,指出了需要继续进行的工作。     

Designing a location update strategy for free-moving and network-constrained objects with varying velocity

Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we focus on designing efficient update strategies for two important types of moving objects, free-moving objects （FMOs） and network-constrained objects （NCOs）, which are classified based on object movement models. For FMOs, we develop a novel update strategy, namely the FMO update strategy （FMOUS）, to explicitly indicate a time point at which the object needs to update location information. As each object knows in advance when to update （meaning that it does not have to continuously check）, the processing overhead can be greatly reduced. In addition, the FMO update procedure （FMOUP） is designed to efficiently process the updates issued from moving objects. Similarly, for NCOs, we propose the NCO update strategy （NCOUS） and the NCO update procedure （NCOUP） to inform each object when and how to update location information. Exten- sive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.