Learning with hypermedia: The influence of representational formats and different levels of learner control on performance and learning behavior

In this paper, two experiments on the use of hypermedia environments for learning about probability theory are reported. In Experiment 1a it was tested whether multimedia design principles (multimedia principle, modality principle, redundancy principle) are valid in hypermedia environments, despite the fact that hypermedia offers more learner control than multimedia. The results showed only little evidence for this validity, although the hypermedia environment entailed only a rather low level of learner control. In Experiment 1b it was investigated how learner control affects performance and how its possible impact is moderated by learners’ prior knowledge. A high level of learner control positively affected the effectiveness of instruction only with regard to intuitive knowledge, but was at the same time accompanied by large increases in learning time, thereby rendering the instruction inefficient. Unexpectedly, effects of learner control were not moderated by students’ prior knowledge. The results imply that the idea to use multimedia design principles for hypermedia learning is too simple and that the benefits and drawbacks of learner control depend heavily on learning objectives and time constraints.     

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner’s performance and paves way for offering personalized learning experiences.     

A review of recent advances in learner and skill modeling in intelligent learning environments

In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.     

Adapting to intelligence profile in an adaptive educational system

Learning characteristics, as informed by research, vary for each individual learner. Research suggests that knowledge is processed and represented in different ways and that students prefer to use different types of resources in distinct ways. However, building Adaptive Educational systems that adapt to different learning characteristics is not easy. Major research questions exist such as: how are the relevant learning characteristics identified, how does modelling of the learner take place and in what way should the learning environment change for users with different learning characteristics?EDUCE is one system that addresses these challenges by using Gardner's theory of multiple intelligences (MI) as the basis for dynamically modelling learning characteristics and for designing instructional material. This paper describes a research study, using EDUCE, that explores the effect of using different adaptive presentation strategies and the impact on learning performance when material is matched and mismatched with learning preferences. The results suggest that students with low levels of learning activity, and who use only a limited number of the resources available, have the most to benefit from adaptive presentation strategies and that surprisingly learning gain increases when they are provided with resources not normally preferred.     

Item difficulty estimation: An auspicious collaboration between data and judgment

The evolution from static to dynamic electronic learning environments has stimulated the research on adaptive item sequencing. A prerequisite for adaptive item sequencing, in which the difficulty of the item is constantly matched to the ability level of the learner, is to have items with a known difficulty level. The difficulty level can be estimated by means of the item response theory (IRT). However, the requirement of a large sample size for calibrating items based on IRT models is not easily met in many practical learning situations. The aim of this paper is to search for relatively simple and fast alternative estimation methods and to review the accuracy of these methods as compared to IRT-based calibration in one single setting, and this for various sample sizes. Using real data, six alternative estimation methods are compared next to IRT-based calibration: proportion correct, learner feedback, expert rating, one-to-many comparison (learner), one-to-many comparison (expert) and the Elo rating system. Results indicate that proportion correct has the strongest relation with IRT-based difficulty estimates, followed by learner feedback, the Elo rating system, expert rating and finally one-to-many comparison. Learner feedback and one-to-many comparison (learner) provide stable estimates even with a small sample size. IRT, proportion correct and the Elo rating system provide reliable estimates, especially with a sample size of 200-250 learners. The alternative estimation methods can be utilized for adaptive item sequencing when IRT-based calibration does not yet provide reliable estimates or can be used as a prior in a Bayesian estimation method.     

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

The popularity of intelligent tutoring systems (ITSs) is increasing rapidly. In order to make learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner or the context. One of the possible adaptation techniques is adaptive item sequencing by matching the difficulty of the items to the learner's knowledge level. This is already accomplished to a certain extent in adaptive testing environments, where the test is tailored to the person's ability level by means of the item response theory (IRT). Even though IRT has been a prevalent computerized adaptive test (CAT) approach for decades and applying IRT in item‐based ITSs could lead to similar advantages as in CAT (e.g. higher motivation and more efficient learning), research on the application of IRT in such learning environments is highly restricted or absent. The purpose of this paper was to explore the feasibility of applying IRT in adaptive item‐based ITSs. Therefore, we discussed the two main challenges associated with IRT application in such learning environments: the challenge of the data set and the challenge of the algorithm. We concluded that applying IRT seems to be a viable solution for adaptive item selection in item‐based ITSs provided that some modifications are implemented. Further research should shed more light on the adequacy of the proposed solutions.     

An activity-theoretical approach to investigate learners’ factors toward e-learning systems

The Internet and World Wide Web have provided opportunities of developing e-learning systems. The development of e-learning systems has started a revolution for instructional content delivering, learning activities, and social communication. Based on activity theory, the purpose of this research is to investigate learners’ attitude factors toward e-learning systems. A total 168 participants were asked to answer a questionnaire. After factor analysis, learners’ attitudes can be grouped four different factors – e-learning as a learner autonomy environment, e-learning as a problem-solving environment, e-learning as a multimedia learning environment, and teachers as assisted tutors in e-learning. In addition, this research approves that activity theory is an appropriate theory for understanding e-learning systems. Furthermore, this study also provides evidence that e-learning as a problem-solving environment can be positively influenced by three other factors.     

What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction

E-learning is emerging as the new paradigm of modern education. Worldwide, the e-learning market has a growth rate of 35.6%, but failures exist. Little is known about why many users stop their online learning after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with e-Learning. This study developed an integrated model with six dimensions: learners, instructors, courses, technology, design, and environment. A survey was conducted to investigate the critical factors affecting learners’ satisfaction in e-Learning. The results revealed that learner computer anxiety, instructor attitude toward e-Learning, e-Learning course flexibility, e-Learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments are the critical factors affecting learners’ perceived satisfaction. The results show institutions how to improve learner satisfaction and further strengthen their e-Learning implementation.     

Learner control in computer-assisted learning*

Cognitive theorists believe that students should be active in organizing their learning. Thus learner control may assist learning directly and promote good strategies. A teaching programme is described that offers to the student control over content, style and level of difficulty. The material was delivered by PET microcomputer and the subject was binary arithmetic. Four groups of young secondary schoolchildren were exposed to different treatments of the same material. The treatments were:

• 1 learner control;
• 1 learner control with advice;
• 1 random program control;
• 1 adaptive program control.

The effect of advice on pupil's choices was clearly evident. The random group performed less well than the other groups but no differences were observed between learner and adaptive control.     

Learning style based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014

The studies on creating learning environments based on differences in learning styles have gained importance in recent years. Learning styles are one of the most important parameters in determining individual differences. Accordingly, traditional web-based learning environments have been replaced by individualized adaptive e-learning environments on the basis of learning styles which are more innovative. This study deals with the content analysis of the recent studies on Adaptive Educational Hypermedia (AEH) based on learning styles. 69 articles published from 2005 to 2014 were obtained through a comprehensive and detailed review. Afterwards, these studies were subjected to document analysis. The studies were categorized under the titles of purpose, nature, method, characteristics of examinees, level, data collection tool, learner modelling, learning styles, subject, and findings. Some of the studies offered a framework or proposed a model for AEH while others focused on the influence of AEH on academic achievement and learning outputs as well as learning satisfaction. This study examines the existing tendencies and gaps in the literature and discusses the potential research topics.     

Research in online learning environments: Priorities and methodologies

Due to increasing demand for education and training in the information age, online learning and teaching is becoming a necessity in our future. However, lack of research goals to understand impact of online learning environments on students is a problem in research on online learning environments. We identified four main research goals to pursue in online learning environments based on their impact on learner achievement, engagement, and retention (opposite of attrition). Those goals are (a) enhancing learner engagement & collaboration, (b) promoting effective facilitation, (c) developing assessment techniques, and (d) designing faculty development programs. Current promising work in those areas is presented. Four methods that are common in the instructional technology literature are recommended to pursue those goals. Formative research and developmental research are relevant for all four. Although appropriate for any of the goals, experimental research is a better fit for goals b and c, and activity theory is useful for goals a and b.     

A NEW E‐LEARNING ACHIEVEMENT EVALUATION MODEL BASED ON ROUGH SET AND SIMILARITY FILTER

The trend of utilizing information and Internet technologies as teaching and learning tools is rapidly expanding into education. E‐learning is one of the most popular learning environments in the information era. The Internet enables students to learn without limitations of space and time. Furthermore, the learners can repeatedly review the context of a course without the barrier of distance. Recently, student‐centered instruction has become the primary trend in education, and the e‐learning system, which is considered with regard to of personalization and adaptability, is more and more popular. By means of e‐learning systems, teachers can adjust the learning schedule instantly for each learner according to a student's achievements and build more adaptive learning environments. Sometimes, teachers give biased assessments of students’ achievements under uncontrollable conditions (i.e., tiredness, preference) and are in dire need of overcoming this predicament. To solve the drawback mentioned, a new model to evaluate learning achievements based on rough set and similarity filter is proposed. The proposed model includes four facets: (1) select important features (attributes) to enhance classification performance by feature selection methods; (2) utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data; (3) select linguistic values for each feature and delete inconsistent data using the similarity threshold (similarity filter); and (4) generate rules based on rough set theory (RST). The practical e‐learning achievement data sets are collected by an e‐learning online examination system from a university in Taiwan. To verify our model, the performances of the proposed model are compared with the listing models. Results of this study demonstrate that the proposed model outperforms the listing models.     

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.     

Who benefits from virtuality?

For educational software to take advantage of contemporary views of learning, instructional designers need to employ design models that incorporate the variety of ideas that are based on constructivist frameworks for developing learning environments. These environments, if well designed, can support learner construction of knowledge, however, such frameworks are based upon arguments that learners should be placed in authentic environments that incorporate sophisticated representations of context through such constructs as “virtual worlds”. Within these environments the learner is supported by visual metaphors constructed to represent the information structure and how the “world” operates. This paper will discuss the framework employed in the development of several virtual solutions and the process by which they were constructed.     

Can instruction as such affect learning? The case of learner control

This study examined the role of learners’ perceptions in a learner-controlled computer-based learning environment. Computer-based learning environments that offer learner control (LC) to the learners are assumed to enhance motivation and learning outcomes. Recently, the focus of LC research has shifted from measuring the direct effect of LC on learning towards focusing on the underlying mechanisms of effective LC and determining under which conditions LC is most effective (Corbalan, Kester, & van Merriënboer, 2009). There is considerable agreement that learners’ skills, perceptions and the experienced cognitive load interactively affect LC as instructional strategy. For this study, 165 first-year university students participated in an on-line English learning course on verb conjugation. We investigated the effect of learners’ perceptions of LC by comparing the learning outcomes and motivation of learners that received additional instructions on learner control with a group of learners that did not receive additional instruction. Learner characteristics, such as prior knowledge, working memory capacity, self-efficacy and cognitive load, were taken into account. The results indicate that it is not instruction as such, but rather satisfaction with the degree of control that affects learning outcomes and motivation. We suggest that instruction as such does not suffice to enhance perception of control, and that learners’ perceptions play a mere role in the effectiveness of instructional strategies such as learner control.     

Group online adaptive learning

Sharing information among multiple learning agents can accelerate learning. It could be particularly useful if learners operate in continuously changing environments, because a learner could benefit from previous experience of another learner to adapt to their new environment. Such group-adaptive learning has numerous applications, from predicting financial time-series, through content recommendation systems, to visual understanding for adaptive autonomous agents. Here we address the problem in the context of online adaptive learning. We formally define the learning settings of Group Online Adaptive Learning and derive an algorithm named Shared Online Adaptive Learning (SOAL) to address it. SOAL avoids explicitly modeling changes or their dynamics, and instead shares information continuously. The key idea is that learners share a common small pool of experts, which they can use in a weighted adaptive way. We define group adaptive regret and prove that SOAL maintains known bounds on the adaptive regret obtained for single adaptive learners. Furthermore, it quickly adapts when learning tasks are related to each other. We demonstrate the benefits of the approach for two domains: vision and text. First, in the visual domain, we study a visual navigation task where a robot learns to navigate based on outdoor video scenes. We show how navigation can improve when knowledge from other robots in related scenes is available. Second, in the text domain, we create a new dataset for the task of assigning submitted papers to relevant editors. This is, inherently, an adaptive learning task due to the dynamic nature of research fields evolving in time. We show how learning to assign editors improves when knowledge from other editors is available. Together, these results demonstrate the benefits for sharing information across learners in concurrently changing environments.

     

Bayesian knowledge tracing,logistic models,and beyond: an overview of learner modeling techniques

Learner modeling is a basis of personalized, adaptive learning. The research literature provides a wide range of modeling approaches, but it does not provide guidance for choosing a model suitable for a particular situation. We provide a systematic and up-to-date overview of current approaches to tracing learners’ knowledge and skill across interaction with multiple items, focusing in particular on the widely used Bayesian knowledge tracing and logistic models. We discuss factors that influence the choice of a model and highlight the importance of the learner modeling context: models are used for different purposes and deal with different types of learning processes. We also consider methodological issues in the evaluation of learner models and their relation to the modeling context. Overall, the overview provides basic guidelines for both researchers and practitioners and identifies areas that require further clarification in future research.     

基于项目反应理论自适应测试系统的设计实现

根据自适应学习的特点,对项目反应理论的参数模型进行分析,提出极大似然估算法计算学习者能力参数。并在分析自适应测试系统的体系结构及系统主要功能模块的基础上,构建该测试系统;并提出了测试系统中首题估算策略、信息间隔量估算策略,从而提高出题速度和计算速度;并根据学习者的实际测试情况得出难度系数和特质水平,实现了难度系数算法和学习者特征水平计算算法。在此基础上开发了一个基于计算机应用基础课程的自适应测试系统。     

Web-based teaching and learner control: a research review

The premise of this paper is that the effectiveness of web in contributing to learning will be a function of web-model alignment and the appropriateness of the model to a particular learning situation. We begin with a discussion of the most commonly advocated models of learning. Then we review the research evidence on learner control in a web-based teaching environment and the conditions under which it can most effectively facilitate rather than impede learning. Future research directions are discussed as well.     

Online formative assessment in higher education: A review of the literature

As online and blended learning has become common place educational strategy in higher education, educators need to reconceptualise fundamental issues of teaching, learning and assessment in non traditional spaces. These issues include concepts such as validity and reliability of assessment in online environments in relation to serving the intended purposes, as well as understanding how formative assessment functions within online and blended learning. This article provides a systematic qualitative review of the research literature on online formative assessment in higher education. As an integrative narrative review, the method applied in this review entailed systematic searching, reviewing, and writing this review of the literature to bring together key themes and findings of research in this field. The authors applied qualitative thematic criteria in selecting and reviewing the available literature from which they focused on identifying and analyzing the core themes that are central to the concept of formative assessment with a key focus on application of formative assessment within blended and online contexts. Various techniques were identified for formative assessment by the individual, peers and the teacher, many of which were linked with online tools such as self-test quiz tools, discussion forums and e-portfolios. The benefits identified include improvement of learner engagement and centrality in the process as key actors, including the development of a learning community. The key findings are that effective online formative assessment can foster a learner and assessment centered focus through formative feedback and enhanced learner engagement with valuable learning experiences. Ongoing authentic assessment activities and interactive formative feedback were identified as important characteristics that can address threats to validity and reliability within the context of online formative assessment.