Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

e‐Learning recommender system for a group of learners based on the unified learner profile approach

In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.     

Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT

In this study, an innovative adaptive and intelligent web based e-learning system, UZWEBMAT (Turkish abbreviation of Adaptive and INtelligent WEB based MAThematics teaching–learning system) was designed, developed and implemented. This e-learning system was intended for learning and teaching secondary school level permutation-combination-binomial expansion and probability subjects. Content which was prepared according to Turkish curriculum for secondary school mathematics course was transformed into learning objects in three different ways in accordance with VAK (Visual–Auditory–Kinesthetic) learning styles. Primary/secondary/tertiary learning styles of learners registering the system are determined and each learner receives the content appropriate for his/her dominant learning style. Also, they can be directed to contents of other styles according to their performances thanks to an expert system. Learning objects constituting the content were prepared according to constructivist approach. An active role for the learner was the purpose. Tips and intelligent solution supports within the learning objects were presented with expert system support to the learners. With this structure, UZWEBMAT bears the characteristics of intelligent tutoring system as well as an adaptive e-learning environment. All the movements of learners studying with UZWEBMAT are recorded and the necessary information is reported to both learners and teachers in a visualized way.     

Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE

In this paper we present an Adaptive Educational Hypermedia prototype, named INSPIRE. The approach employed in INSPIRE emphasizes the fact that learners perceive and process information in very different ways, and integrates ideas from theories of instructional design and learning styles. Our aim is to make a shift towards a more 'learning-focused' paradigm of instruction by providing a sequence of authentic and meaningful tasks that matches learner' preferred way of studying. INSPIRE, throughout its interaction with the learner, dynamically generates learner-tailored lessons that gradually lead to the accomplishment of learner's learning goals. It supports several levels of adaptation: from full system-control to full learner-control, and offers learners the option to decide on the level of adaptation of the system by intervening in different stages of the lesson generation process and formulating the lesson contents and presentation. Both the adaptive and adaptable behavior of INSPIRE are guided by the learner model which provides information about the learner, such as knowledge level on the domain concepts and learning style. The learner model is exploited in multiple ways: curriculum sequencing, adaptive navigation support, adaptive presentation, and supports system's adaptable behavior. An empirical study has been performed to evaluate the adaptation framework and assess learners' attitudes towards the proposed instructional design.     

基于改进教学算法的车间作业调度问题

为求解车间作业调度问题,提出一种基于个体差异化自学习的改进教学算法.针对教学算法局部搜索能力不高的缺陷, 提出学生不仅应向能力好的学习者学习,亦应进行有差异的自我学习.通过学习者的完工时间评估学生的学习能力,提出学习次数概念,并设计自学习算子,完善学生阶段的更新,提高算法的局部搜索能力.最后,对OR-Library中的标准仿真实例进行实验,结果表明改进教学算法是有效的,其在收敛精度和鲁棒性能上均有较好的提高.     

Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings

Mobile technologies can support learning across different contexts as their portability enables them to be used by the learner in whichever context she or he is in. They can be particularly beneficial in informal and semiformal contexts where learners have more control over their learning goals and where motivation is often high. Inquiries in informal contexts are likely to be personally relevant in terms of topics of interest and capitalise on learners' location as learners decide what, where, when and whether to learn. There is considerable interest in how such benefits can be harnessed for more formal learning and one challenge is how to make inquiries personally relevant in such contexts. However, there is little literature that considers the structure needed to support informal and semiformal inquiry learning. This paper contributes to that literature by examining dimensions for researchers and designers to consider investigating or developing support for inquiries in informal or semiformal settings.The paper examines two case studies of inquiry learning in contrasting settings in order to understand more about learner control and how technology can support learners' inquiries. Case study one considers the use of web based software to support science inquiry learning by 14–15 year olds in a semiformal context, whilst the second case study reports on informal adult learners using their own mobile technologies to learn about landscape. These case studies are compared and contrasted in terms of the dimensions of learner control, location of learning, and the different support mechanisms for inquiry learning and a framework is proposed for considering these dimensions.     

Machine Learning for Information Extraction in Informal Domains

We consider the problem of learning to perform information extraction in domains where linguistic processing is problematic, such as Usenet posts, email, and finger plan files. In place of syntactic and semantic information, other sources of information can be used, such as term frequency, typography, formatting, and mark-up. We describe four learning approaches to this problem, each drawn from a different paradigm: a rote learner, a term-space learner based on Naive Bayes, an approach using grammatical induction, and a relational rule learner. Experiments on 14 information extraction problems defined over four diverse document collections demonstrate the effectiveness of these approaches. Finally, we describe a multistrategy approach which combines these learners and yields performance competitive with or better than the best of them. This technique is modular and flexible, and could find application in other machine learning problems.     

Collaborative spaces for GIS-based multimedia cartography in blended environments

The interaction spaces between instructors and learners in the traditional face-to-face classroom environment are being changed by the diffusion and adoption of many forms of computer-based pedagogy. An integrated understanding of these evolving interaction spaces together with how they interconnect and leverage learning are needed to develop meaningful strategies for effective teaching and learning. The 18i collaborative interaction spaces model was designed based on constructivist principles, and describes 18 mixed instructor–learner spaces contextualized at a finer operational scale that makes explicit a wider range of interactions. The model was implemented during the life cycle of an undergraduate GIS-based multimedia cartography course. One output was the generation of a repository of rule-based trajectory plans for rapid planning and problem solving. The model provides an integrated workflow to manage course contents, products, interactions, individuality, and learning styles in blended environments.     

面向分布式在线学习的共享数据方法

分布式数据流已成为现代数据驱动应用产生数据的主要形式,而局部节点的数据虽然独立存储,但彼此之间是相互关联的,因此如何高效地共享局部节点数据来构建全局学习器是分布式在线学习的关键问题.针对此问题,提出一种分布式在线学习的数据共享解决方案,包括基于指数损失的半监督聚类方法和基于协方差矩阵与均值向量的数据共享方法,并证明重构数据集的累计绝对误差小于给定绝对误差界的概率下界.实验表明:所提出的方法可以使节点间的共享数据量维持在一个较低的水平,同时保证基于重构数据训练得到的学习器具有很好的泛化学习能力.     

Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution

Extreme learning machine for single-hidden-layer feedforward neural networks has been extensively applied in imbalanced data learning due to its fast learning capability. Ensemble approach can effectively improve the classification performance by combining several weak learners according to a certain rule. In this paper, a novel ensemble approach on weighted extreme learning machine for imbalanced data classification problem is proposed. The weight of each base learner in the ensemble is optimized by differential evolution algorithm. Experimental results on 12 datasets show that the proposed method could achieve more classification performance compared with the simple vote-based ensemble method and non-ensemble method.

     

Elo-based learner modeling for the adaptive practice of facts

We investigate applications of learner modeling in a computerized adaptive system for practicing factual knowledge. We focus on areas where learners have widely varying degrees of prior knowledge. We propose a modular approach to the development of such adaptive practice systems: dissecting the system design into an estimation of prior knowledge, an estimation of current knowledge, and the construction of questions. We provide a detailed discussion of learner models for both estimation steps, including a novel use of the Elo rating system for learner modeling. We implemented the proposed approach in a system for practising geography facts; the system is widely used and allows us to perform evaluation of all three modules. We compare the predictive accuracy of different learner models, discuss insights gained from learner modeling, as well as the impact different variants of the system have on learners’ engagement and learning.     

Variations on U-shaped learning

The paper deals with the following problem: is returning to wrong conjectures necessary to achieve full power of algorithmic learning? Returning to wrong conjectures complements the paradigm of U-shaped learning when a learner returns to old correct conjectures. We explore our problem for classical models of learning in the limit from positive data: explanatory learning (when a learner stabilizes in the limit on a correct grammar) and behaviourally correct learning (when a learner stabilizes in the limit on a sequence of correct grammars representing the target concept). In both cases we show that returning to wrong conjectures is necessary to achieve full learning power. In contrast, one can modify learners (without losing learning power) such that they never show inverted U-shaped learning behaviour, that is, never return to old wrong conjecture with a correct conjecture in-between. Furthermore, one can also modify a learner (without losing learning power) such that it does not return to old “overinclusive” conjectures containing non-elements of the target language. We also consider our problem in the context of vacillatory learning (when a learner stabilizes on a finite number of correct grammars) and show that each of the following four constraints is restrictive (that is, reduces learning power): the learner does not return to old wrong conjectures; the learner is not inverted U-shaped; the learner does not return to old overinclusive conjectures; the learner does not return to old overgeneralizing conjectures. We also show that learners that are consistent with the input seen so far can be made decisive: on any text, they do not return to any old conjectures—wrong or right.     

Selecting informative data for defect segmentation from imbalanced datasets via active learning

The storage and labeling of industrial data incur significant costs during the development of defect detection algorithms. Active learning solves these problems by selecting the most informative data among the given unlabeled data. The existing active learning methods for image segmentation focus on studying natural images and medical images, with less attention given to industrial images, and little research has been performed on imbalanced data. To solve these problems, we propose an active learning framework to selecting informative data for defect segmentation under imbalanced data. In the initialization stage, the framework uses self-supervised learning to initialize the data so that the initialization data contain more defect data, thereby solving the cold-start problem. During the iterative stage, we design the main body of the active learning framework, which is composed of a segmentation learner and a reconstruction learner. These learners use supervised learning to further improve the framework’s ability to select informative data. The experimental results obtained on public and self-owned datasets show that the framework can save 70% of the required storage space and greatly reduce the cost of labeling. The intersection over union value proves that the designed framework can achieve the equivalent effect of labeling the whole dataset by labeling partial data.     

Designing learner-controlled educational interactions based on learning/cognitive style and learner behaviour

Recently, research in individual differences and in particular, learning and cognitive style, has been used as a basis to consider learner preferences in a web-based educational context. Modelling style in a web-based learning environment demands that developers build a specific framework describing how to design a variety of options for learners with different approaches to learning. In this paper two representative examples of educational systems, Flexi-OLM and INSPIRE, that provide learners a variety of options designed according to specific style categorisations, are presented. Experimental results from two empirical studies performed on the systems to investigate learners' learning and cognitive style, and preferences during interaction, are described. It was found that learners do have a preference regarding their interaction, but no obvious link between style and approaches offered, was detected. Derived from an examination of this experimental data, we suggest that while style information can be used to inform the design of learning environments that accommodate learners' individual differences, it would be wise to recommend interactions based on learners' behaviour. Learning environments should allow learners or learners' interaction behaviour to select or trigger the appropriate approach for the particular learner in the specific context. Alternative approaches towards these directions are also discussed.     

Web-based learning programs: Use by learners with various cognitive styles

To consider how Web-based learning program is utilized by learners with different cognitive styles, this study presents a Web-based learning system (WBLS) and analyzes learners’ browsing data recorded in the log file to identify how learners’ cognitive styles and learning behavior are related. In order to develop an adapted WBLS, this study also proposes a design model for system designers to tailor the preferences linked with each cognitive style. The samples comprise 105 third-grade Accounting Information System course students from a technology university in central Taiwan. Analytical results demonstrate that learners with different cognitive styles have similar but linear learning approaches, and learners with different cognitive styles adopt different navigation tools to process learning.     

Improving problem solving and creativity through use of complex-dynamic simulations

This article presents an instructional method to improve problem solving and creativity by employing computer-based simulations. The instructional method described is based upon empirical research conducted by the authors. The simulation presents contextually meaningful problem situations that require learners to analyze and prepare solution proposal(s). Following the learner input, the simulation assesses the proposal and offers back to the learners the consequences of their decisions while also iteratively updating the situational conditions. This type of simulation, unlike conventional simulations that are used for acquisition of knowledge, is complex-dynamic, requiring the learner to fully employ their knowledge base by constructing solutions to domain-specific problems. The focus of complex-dynamic simulations is to improve and elaborate learner cognitive abilities employed in the service of problem solving and creativity.     

Personalized e-learning system using Item Response Theory

Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.     

融入学习者模型在线学习资源协同过滤推荐方法

在线教育存在“信息迷航”问题,而传统的信息推荐方法往往忽视教育的主体—学习者的特征。本文依据教育教学理论,根据在线教育平台中的学习者相关数据,研究构建了适用于在线学习资源个性化推荐的学习者模型。以协同过滤推荐方法为切入点,融合学习者模型中的静态特征和动态特征对协同过滤方法进行改进,建立融入学习者模型的在线学习资源协同过滤推荐方法。以2020年3~7月时间段的东北石油大学“C程序设计”课程学生的真实学习数据和行为数据为数据集,对本文提出的方法进行验证和对比,最后证明本文提出的方法在性能上均优于对比方法。