Investigating university student preferences and beliefs about learning in the web-based context

Psychological studies have shown that personal beliefs about learning and environmental preferences affect learning behaviors. However, these learner characteristics have not been widely discussed in the web-based context. By developing questionnaires, this study attempted to detect learners’ web-based learning environmental preferences (WLEP) and beliefs about web-based learning (BWL). The scope of WLEP focused on the pedagogical dimension of the web-based learning environment, while BWL concerned the attributes and control factors of the web-based learning. There were about five hundreds of Taiwan university students participating in the study. Through factor analysis, the scales discussed in the study revealed a satisfactory validity and reliability in assessing students’ preferences and beliefs. Further analyses showed that university students preferred more of individual and structured instructional configurations while expected the outward mode of interaction. In general, students held a rather contextual belief about web-based learning, which was found to be correlated with their environmental preferences.     

A contextual game-based learning approach to improving students' inquiry-based learning performance in social studies courses

Inquiry-based learning, an effective instructional strategy, can be in the form of a problem or task for triggering student engagement. However, how to situate students in meaningful inquiry activities remains to be settled, especially for social studies courses. In this study, a contextual educational computer game is developed to improve students' learning performance based on an inquiry-based learning strategy. An experiment has been conducted on an elementary school social studies course to evaluate the effects of the proposed approach on the inquiry-based learning performances of students with different learning styles. The experimental results indicate that the proposed approach effectively enhanced the students' learning effects in terms of their learning achievement, learning motivation, satisfaction degree and flow state. Furthermore, it is also found that the proposed approach benefited the “active” learning style students more than the “reflective” style students in terms of learning achievement. This suggests the need to provide additional supports to students with particular learning styles in the future.     

Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment

Learner modeling has been used in computer-based learning environments to model learners’ domain knowledge, cognitive skills, and interests, and customize their experiences in the environment based on this information. In this paper, we develop a learner modeling and adaptive scaffolding framework for Computational Thinking using Simulation and Modeling (CTSiM)—an open ended learning environment that supports synergistic learning of science and Computational Thinking (CT) for middle school students. In CTSiM, students have the freedom to choose and coordinate use of the different tools provided in the environment, as they build and test their models. However, the open-ended nature of the environment makes it hard to interpret the intent of students’ actions, and to provide useful feedback and hints that improves student understanding and helps them achieve their learning goals. To address this challenge, we define an extended learner modeling scheme that uses (1) a hierarchical task model for the CTSiM environment, (2) a set of strategies that support effective learning and model building, and (3) effectiveness and coherence measures that help us evaluate student’s proficiency in the different tasks and strategies. We use this scheme to dynamically scaffold learners when they are deficient in performing their tasks, or they demonstrate suboptimal use of strategies. We demonstrate the effectiveness of our approach in a classroom study where one group of 6th grade students received scaffolding and the other did not. We found that students who received scaffolding built more accurate models, used modeling strategies effectively, adopted more useful modeling behaviors, showed a better understanding of important science and CT concepts, and transferred their modeling skills better to new scenarios.     

From handheld collaborative tool to effective classroom module: Embedding CSCL in a broader design framework

The field of Computer Supported Collaborative Learning (CSCL) includes designers who emphasize effectiveness, measured via experiments, as well as designers who emphasize context and conduct qualitative research on teaching and learning practices. We conjectured that these two different emphases could be fruitful combined in a research and development process aimed at producing effective CSCL practices. We explored this possibility in a project that adapted a CSCL tool from Chile to serve as the basis of an effective 3-week classroom module for primary school mathematics in the United States. To go from tool to module, we addressed curricular fit, training materials, pedagogical guidance, formative and summative assessments, and logistical support. In conducting the project, we found that effectiveness and contextual research could be conducted simultaneously and yielded complementary insight to this design process, which enabled our project to rapidly move from the base tool towards complete classroom modules. An experiment we conducted after our design iterations showed that students who used the modules learned more about the target content, fractions. A retrospective analysis of our design process suggests that the Integrative Learning Design framework is useful for organizing the complementary components of effectiveness and contextual research in our design process.     

Usage of a mobile social learning platform with virtual badges in a primary school

This study presents and examines SamEx, a mobile learning system used by 305 students in formal and informal learning in a primary school in Singapore. Students use SamEx in situ to capture media such as pictures, video clips and audio recordings, comment on them, and share them with their peers. In this paper we report on the experiences of students in using the application throughout a one-year period with a focus on self-directedness, quality of contributions, and answers to contextual question prompts. We examine how the usage of tools such as SamEx predicts students' science examination results, discuss the role of badges as an extrinsic motivational tool, and explore how individual and collaborative learning emerge. Our research shows that the quantity and quality of contributions provided by the students in SamEx predict the end-year assessment score. With respect to specific system features, contextual answers given by the students and the overall likes received by students are also correlated with the end-year assessment score.     

Mapping queries to the Linking Open Data cloud: A case study using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.     

Extracting Hidden Context

Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and communication network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, off-line learners tend to be ineffective in domains with hidden changes in context as they assume that the training set is homogeneous. An off-line, meta-learning approach for the identification of hidden context is presented. The new approach uses an existing batch learner and the process of contextual clustering to identify stable hidden contexts and the associated context specific, locally stable concepts. The approach is broadly applicable to the extraction of context reflected in time and spatial attributes. Several algorithms for the approach are presented and evaluated. A successful application of the approach to a complex flight simulator control task is also presented.     

Perspectives on personal learning environments held by vocational students

This article focuses on personal learning environments (PLEs). The idea with PLEs is to put students in a more central position in the learning process by allowing them to design their own learning environments and by emphasising the self-regulated nature of the learning. This study describes the structure, functions and challenges of PLEs made by 33 students from vocational and polytechnic level schools in Eastern Finland after the first year of using them. Document and artefact analysis revealed that students built their PLEs for: mirroring the conventional learning environment; as an environment for reflection; as an environment to showcase skills; and as an environment for collaboration and networking. Student reflections indicate that building and using a PLE is a challenging task which requires specific teacher and pedagogical support. Designing a PLE demands both ICT skills and an awareness of one’s own learning methods.     

The impact of learner attributes and learner choice in an agent-based environment

This study examined the impact of learners’ attributes (gender and ethnicity) on their choice of a pedagogical agent and the impact of the attributes and choice on their perceptions of agent affability, task-specific attitudes, task-specific self-efficacy, and learning gains. Participants were 210 high-school male and female, Caucasian and Hispanic students who worked at computer-based algebra integrated with pedagogical agents. The results indicated, first, that students preferentially chose a same-gender agent and a same-ethnicity agent. Second, males who chose an agent showed more positive attitudes toward working at the learning environment than did males who were assigned to an agent whereas females who were assigned to an agent showed more positive attitudes than did females who chose an agent. Third, Hispanic students showed more positive attitudes toward working at the learning environment than Caucasians. Fourth, females perceived the agent as significantly more affable than did males; Hispanics perceived the agent as significantly more affable than did Caucasians. Last, learner attributes and choice did not affect learning gains in the agent-based environment; rather, the participants overall significantly increased their performances after the intervention.     

The effectiveness of adaptive difficulty adjustments on students' motivation and learning in an educational computer game

Computer games that adaptively adjust difficulty are used to continuously challenge players according to their abilities. The adjustment of difficulty occurs automatically in response to a game's ongoing assessment of a player's performance. This approach to difficulty adjustment is likely to be of value in educational computer games as a means of scaffolding learning for students. However, there is limited research evaluating the effectiveness of educational computer games with adaptive difficulty adjustment when compared to non-adaptive difficulty adjustment. To expand on this research a quasi-experimental study was designed to isolate the impact of the difficulty adjustment game element on motivation and learning. A total of 234 secondary school students were allocated to one of three activities involving learning about Spanish cognates: an adaptive difficulty adjustment game, an incremental difficulty adjustment game that was non-adaptive, and a written activity. The three learning activities were designed following the same learning and motivation theories. The two games were identical apart from the difficulty adjustment mechanism. The results for motivation indicated that all students experienced high levels and there was no significant difference between the three learning activities. The pre- and post-tests results for learning indicated that significantly higher learning outcomes were achieved by students who played the adaptive game. Analysis of a game log recording the correctness of students' responses indicated that the adaptive difficulty adjustment game, in contrast to the non-adaptive incremental difficulty adjustment game, provided a scaffolding structure to enhance student learning.     

The impact of media on collaborative learning in virtual settings: The perspective of social construction

This study examines an alternative function of information sharing – social construction of meaning. Drawing on social construction, social interaction, and task closure theories, we explored the influence of both the media environment in which students are situated and the medium that group members choose to communicate with one another on the intricate relationships among breadth of information sharing, depth of information sharing, and performance of computer-supported collaborative learning (CSCL). A total of 126 students participated in the experiment – including 63 students (15 groups of four students and one group of three students) in the control and experimental groups respectively. Our findings show that most of the proposed hypotheses are supported. Intersubjective interpretation underlies groups information sharing and plays a key role in student learning performance. Evidence shows that when facing a relatively complex task in multimedia environments, students who choose to utilize a medium lower in social presence (i.e., electronic information sharing) are more likely to achieve task closure than a medium higher in social presence (i.e., verbal information sharing). This in turn leads to higher learning performance. The implications for both theory and pedagogy are also discussed.     

Shared orchestration within and beyond the classroom

With the introduction of personal computer devices such as smartphones and tablets into school classrooms, teachers have the added task of orchestrating complex dynamic systems of students interacting with networked technologies. One approach to managing the interactions is to add an orchestration system that enables a teacher view and control each student device. An alternative is to share responsibility for orchestration between the teacher, the students, and the technology. In this form of orchestration, the teacher and all the students have similar computer toolkits designed to guide the students through a productive learning activity. An advantage is that the teacher can hand over control to the students to continue the learning activity outside the classroom, assisted by the Activity Guide software.     

Predicting student performance in a blended MOOC

Predicting student performance is a major tool in learning analytics. This study aims to identify how different measures of massive open online course (MOOC) data can be used to identify points of improvement in MOOCs. In the context of MOOCs, student performance is often defined as course completion. However, students could have other learning objectives than MOOC completion. Therefore, we define student performance as obtaining personal learning objective(s). This study examines a subsample of students in a graduate‐level blended MOOC who shared on‐campus course completion as a learning objective. Aggregated activity frequencies, specific course item frequencies, and order of activities were analysed to predict student performance using correlations, multiple regressions, and process mining. All aggregated MOOC activity frequencies related positively to on‐campus exam grade. However, this relation is less clear when controlling for past performance. In total, 65% of the specific course items showed significant correlations with final exam grade. Students who passed the course spread their learning over more days compared with students who failed. Little difference was found in the order of activities within the MOOC between students who passed and who failed. The results are combined with course evaluations to identify points of improvement within the MOOC.     

Development of a contextual decision-making game for improving students' learning performance in a health education course

Heath education is an important component of the curriculum for fostering children's correct health knowledge and good daily life habits. However, educators have indicated that most children might fail to realize the importance and meaning of health education content owing to the lack of authentic scenarios and daily life experience. With the advancement of computer technologies, researchers have tried to develop multimedia learning content in order to improve students' learning performance. Among various technology-enhanced learning alternatives, digital game-based learning has been recognized as a highly potential approach to motivating students. However, several previous studies have indicated that, without properly incorporating learning content into game scenarios, the effectiveness of digital game-based learning might not be as good as expected in comparison with conventional technology-enhanced learning. In this study, a contextual digital game was developed for improving students' learning performance in an elementary school health education course. A quasi experiment was conducted to evaluate the effectiveness of the proposed approach by situating the experimental group in the game-based learning scenario and the control group to learn with conventional e-books. The experimental results showed that the proposed approach not only improved the students' learning motivation, but also their learning achievement and problem-solving competences. Moreover, the significant two-way interaction suggested that the contextual game-based learning approach benefited the higher motivation students more than the lower motivation ones in terms of the advanced knowledge, showing the importance and potential of applying contextual games to health education activities.     

The role of feedback and self-efficacy on web-based learning: The social cognitive perspective

The social cognitive perspective of self-regulated learning suggests that effective learning is determined by the interactions among personal, behavioral, and environmental influences; particularly, high self-regulated learners hold higher motivation (personal), apply better learning strategies (behavioral) and respond to environmental demand more appropriately (environmental). The study thus uses the social cognitive perspective to explore the role of self-efficacy (personal), student feedback behavior, use of learning strategies (behavioral), performance and receiving feedback (environmental) in Web-based learning. There were 76 university students participated in this study. Both quantitative and qualitative methods were applied for data analysis. The results supported that self-efficacy predicted student use of learning strategies and related to elaborated feedback behavior (personal → behavioral). High self-efficacy students applied more high-level learning strategies, such as elaborative strategy and critical thinking. Students who provided elaborated feedback also had higher self-efficacy than those who did not. Moreover, receiving elaborative feedback significantly promoted student self-efficacy (environmental → personal), while receiving knowledge of correct response improved student performance. However, the results indicated that feedback behaviors did not predict academic performance, which may be interfered by modeling effects.     

基于深度学习下的微课设计模型

从重构微课概念入手,从其课程属性、教学属性和资源属性出发,以目标、内容、情境、活动、交互、资源、界面和评价为设计要素,基于深度学习理论与ARCS动机模型,完成微课设计理论模型的构建。利用该模型实现微课设计的优化,使微课能够激发并维持学习者的学习动机,完成知识的理解与建构,让学习者实现深度学习的目标。     

Transfer in variable-reward hierarchical reinforcement learning

Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs.     

Development of an adaptive learning system with two sources of personalization information

Previous research of adaptive learning mainly focused on improving student learning achievements based only on single-source of personalization information, such as learning style, cognitive style or learning achievement. In this paper, an innovative adaptive learning approach is proposed by basing upon two main sources of personalization information, that is, learning behavior and personal learning style. To determine the initial learning styles of the students, the [Keefe, J. W. (1987). Learning Styles: Theory and Practice. Reston, VA: National Association of Secondary School Principals.] questionnaire is employed in our approach. To more precisely reflect the learning behaviors of each student, the interactions and learning results of each student are analyzed when adjusting the subject materials. Based on the innovative approach, an adaptive learning system has been developed; moreover, an experiment was conducted to evaluate the performance of our approach. By analyzing the results from three groups of students using different adaptive learning approaches, it can be found that the innovative approach is helpful in improving both the learning achievement and learning efficiency of individual students.