Learning from abstract and contextualized representations: The effect of verbal guidance

An experiment examined the effects of providing explicit verbal guidance to learners in integrating information with abstract or contextualized representations during computer-based learning of engineering. Verbal guidance supported learners in identifying correspondences and making mental connections among multiple textual and diagrammatic representations. Results from a 2 (abstract (A) or contextualized (C) representation) × 2 (no guidance or guidance) design showed that without guidance, abstract representations led to better transfer than contextualized representations. Moreover, learners in the contextualized representation group benefitted from the guidance, while the abstract representation group did not benefit from guidance. These findings suggest that abstract representations promote the development of deep, transferrable knowledge and that verbal guidance denoting correspondences among representations can facilitate learning when less effective representational formats are utilized.     

Personalized curriculum sequencing utilizing modified item response theory for web-based instruction

Curriculum sequencing is an important research issue for Web-based instruction systems because no fixed learning pathway will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanism to assist on-line Web-based learning and adaptively provide learning pathways. However, although most personalized systems consider learner preferences, interests and browsing behavior in providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended courseware are matched to each other or not. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning, thus reducing learning effect. Besides, the problem of concept continuity of learning pathways also needs to be considered while implementing personalized curriculum sequencing. Smoother learning pathways increase learning effect, avoiding unnecessarily difficult concepts. This paper presents a prototype of personalized Web-based instruction system (PWIS) based on the proposed modified Item Response Theory (IRT) to perform personalized curriculum sequencing through simultaneously considering courseware difficulty level, learner's ability and the concept continuity of learning pathways during learning. In the proposed modified IRT, the information function is revised to consider the concept continuity of learning pathway as well as considering the difficulty level of courseware and individual learner ability. Experiment results indicate that applying the proposed modified IRT for Web-based learning can construct suitable learning pathway to learners for personalized learning, and help them to learn more effectively.     

LIM-G: Learner-initiating instruction model based on cognitive knowledge for geometry word problem comprehension

Computer-assisted instruction systems have been broadly applied to help students solve math word problem. The majority of such systems, which are based on an instructor-initiating instruction strategy, provide pre-designed problems for the learners. When learners are asked to solve a word problem, the system will instruct the learners what to do. However, systems employing an instructor-initiating instruction strategy offer little help to advanced learners or to learners encountering problems that are not in the pre-designed database. Therefore, in this study, a learner-initiating instruction model (LIM-G) is proposed to help learners’ comprehension of geometry word problems. Geometry word problems are math word problems involving geometric concepts. Many researches indicate that learners encounter difficulties while comprehending math word problems. In this model, a learner can seek help with any geometry word problem he is interested in. Based on a learner-initiating instruction strategy, LIM-G first comprehends the problem and then gives the learner the telegraphic and diagrammatic representations of the problem, which are more intuitive to understand. For LIM-G, the comprehension mechanism plays a critical role in solving word problems. For this study, a system is built based on LIM-G. In this system, the cognitive knowledge needed for comprehending geometry word problem is constructed with an ontology-based tool called InfoMap. Using cognitive knowledge and frame-template structures, the system can extract the relevant concepts in geometry word problems for comprehension.     

The format of problem representation for in‐game learning supports

A promising method to support game‐based learning is to facilitate learners' externalization of cognitive and metacognitive processes. Externalizing Problem Representation (EPR) refers to a cognitive behaviour in which a learner constructs her own representations overtly. The purpose of this study is to investigate whether learning supports promoting EPR enhance qualitative understanding and quantitative proficiency in ratios and proportional relationships in a learning game (i.e., E‐Rebuild) context. Specifically, this study investigated the effects of representation format in problem representation on qualitative understanding and quantitative proficiency in a learning game context. The results of this study indicate that (a) symbolic learning supports better facilitate comprehension of math concepts and their relations than iconic learning supports in video game contexts, (b) symbolic learning supports better facilitate players' reflection for implicit understanding and promote their math problem‐solving skills, (c) participants in the symbolic learning support group increased significantly in qualitative understanding but not in quantitative proficiency after gameplay, and (d) participants in the iconic learning support group experienced significant growth in quantitative proficiency but not in qualitative understanding after gameplay.     

An experimental research study on the effect of pictorial icons on a user-learner’s performance

This experimental counterbalanced repeated measures study on the effect of three types of icons (abstract, pictorial and drawing) on the learning and performance of two types of learners (abstract and concrete) was conducted during the Spring 2000–Spring 2001 semesters at the University of Central Florida. The counterbalanced lesson followed by a counterbalanced and completely randomized quiz on three levels was field tested in the Spring–Summer 2000 on 37 UCF graduate students and final tested in the Fall 2000–Spring 2001 on 53 UCF graduate student subjects. A general linear model repeated measures ANOVA revealed that icon type had a significant effect on the learning and performance of both types of learners. Pictorial icons resulted in best scores. No significant interaction was found between icon type and learner type even though on a matched condition, abstract learners with abstract icon resulted in better scores than when concrete learners were matched with concrete (pictorial and drawing) icons. Implications for future research are drawn as well as practical applications identified in the field of teaching, learning, training and performance.     

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.     

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.     

Activity sequence modelling and dynamic clustering for personalized e-learning

Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners?? problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.     

The role of spatial ability when fostering mental animation in multimedia learning: An ATI-study

The present Aptitude-Treatment-Interaction (ATI) study investigates the learner characteristic spatial ability (aptitude) and the variation of mental-animation prompts (treatment: no vs. mental-animation prompts). A group of high-school students (N = 94) learned about a biology topic through learner-paced multimedia instruction. Some of the learners received mental-animation prompts and others learned without prompts. A fine-grained analysis with spatial ability as continuous aptitude variable and mental animation as treatment showed a positive learning effect of animation prompts in learning outcomes of processes, but not in knowledge about structures. In addition, spatial ability only modified the relationship between animation prompts and learning when analyzing knowledge about processes. Specifically, only learners of low to medium spatial ability profited from the prompts while learners with very low or high spatial ability had comparable results when learning with or without prompts. In addition, only learners with high spatial ability rated their cognitive load to be significantly higher when learning with prompts. Results align with the assumptions of the production deficiency of learners with low to medium spatial ability, mediation deficiency of learners with very low spatial ability and stable learning performance of learners with high spatial ability whatever the learning situation offers.     

深度学习在代码表征中的应用综述

代码表征是对代码数值化的一种技术,把代码映射为一组连续的实值向量,提取隐藏在代码内部的属性,辅助程序员生成或分析代码,是代码克隆、代码推荐、代码剽窃等软件工程任务的核心技术和研究热点。研究者们对代码表征方面进行了一系列研究,根据源代码抽取信息的方式,分为基于文本的表征、基于语法的表征、基于语义的表征和基于功能的表征;根据表征粒度的大小,分为基于词汇的表征、基于语句的表征、基于函数的表征等不同等级;根据表征方法的不同,分为基于统计的模型、基于自然语言的模型和基于深度学习的模型。对近几年基于深度学习的代码表征研究进展进行了综述,并从表征粒度、表征层次、表征模型、应用场景等方面对现有工作进行了概括、比较和分析。对基于深度学习的代码表征的未来发展趋势进行分析和展望。     

Lifelike Pedagogical Agents for Mixed-initiative Problem Solving in Constructivist Learning Environments

Mixed-initiative problem solving lies at the heart of knowledge- based learning environments. While learners are actively engaged in problem-solving activities, learning environments should monitor their progress and provide them with feedback in a manner that contributes to achieving the twin goals of learning effectiveness and learning efficiency. Mixed-initiative interactions are particularly critical for constructivist learning environments in which learners participate in active problem solving. We have recently begun to see the emergence of believable agents with lifelike qualities. Featured prominently in constructivist learning environments, lifelike pedagogical agents could couple key feedback functionalities with a strong visual presence by observing learners' progress and providing them with visually contextualized advice during mixed-initiative problem solving. For the past three years, we have been engaged in a large-scale research program on lifelike pedagogical agents and their role in constructivist learning environments. In the resulting computational framework, lifelike pedagogical agents are specified by(1) a behavior space containing animated and vocal behaviors,(2) a design-centered context model that maintains constructivist problem representations, multimodal advisory contexts, and evolving problem-solving tasks, and(3) a behavior sequencing engine that in realtime dynamically selects and assembles agents' actions to create pedagogically effective, lifelike behaviors.To empirically investigate this framework, it has been instantiated in a full-scale implementation of a lifelike pedagogical agent for Design-A-Plant, a learning environment developed for the domain of botanical anatomy and physiology for middle school students. Experience with focus group studies conducted with middle school students interacting with the implemented agent suggests that lifelike pedagogical agents hold much promise for mixed-initiative learning.     

语义增强的在线学习行为预测研究

当前学习者的在线学习行为预测研究未充分利用短文本中的语义数据,导致对学习者的学习状态刻画不够全面,严重影响了行为预测的准确性.针对此问题,本文提出了语义增强的在线学习行为预测方法.首先,利用双向长短时记忆网络得到到短文本的语义向量表示;其次,基于学习者的统计、行为和短文本数据得到学习者的特征表征,并利用长短时记忆网络模型构建其学习状态表征;最后,利用学习状态表征预测学习者的学习行为.在11门真实在线课程数据集上的实验表明,本文方法能过有效提升在线学习行为预测的精确度.     

Optimal dynamic decision network model for scientific inquiry learning environment

While recent studies employ heuristic to support learners in scientific inquiry learning environments, this study examined the theoretical and practical aspects of decision-theoretic approach to simultaneous reason about learners’ scientific inquiry skills and provision of adaptive pedagogical interventions across time. In this study, the dynamic learner model, represented by three different Dynamic Decision Network (DDN) models, were employed and evaluated through a three-phase empirical study. This paper discusses how insights gained and lessons learned from the evaluations of a preceding model had led to the improvements of subsequent model; before finalizing the optimal design of DDN model. The empirical studies involved six domain experts, 101 first-year university learners, and dataset from our previous research. Each learner participated in a series of activities including a pretest, a session with INQPRO learning environment, a posttest, and an interview session. For each DDN model, the predictive accuracies were computed by comparing the classifications given by the model with (a) the results obtained from the pretest, posttest, and learner self-rating scores, and (b) classifications elicited by domain experts based on the learner interaction logs and the graphs exhibited by each model.     

Intelligent web-based learning system with personalized learning path guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.     

基于稀疏表示剪枝集成建模的烧结终点位置智能预测

烧结终点位置(BTP)是烧结过程至关重要的参数,直接决定着最终烧结矿的质量.由于BTP难以直接在线检测,因此,通过智能学习建模来实现BTP的在线预测并在此基础上进行操作参数调节对提高烧结矿质量具有重要意义.针对这一实际工程问题,首先提出一种基于遗传优化的Wrapper特征选择方法,可选取使后续预测建模性能最优的特征组合;在此基础上,为了解决单一学习器容易过拟合的问题,提出了基于随机权神经网络(RVFLNs)的稀疏表示剪枝(SRP)集成建模算法,即SRP-ERVFLNs算法.所提算法采用建模速度快、泛化性能好的RVFLNs作为个体基学习器,采用对基学习器基函数与隐层节点数等参数进行扰动的方式来增加集成学习子模型间的差异性;同时,为了进一步提高集成模型的泛化性能与计算效率,引入稀疏表示剪枝算法,实现对集成模型的高效剪枝;最后,将所提算法用于烧结过程BTP的预测建模.工业数据实验表明,所提方法相比于其他方法具有更好的预测精度、泛化性能和计算效率.     

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.     

Virtual CEOs: A blended approach to digital gaming for enhancing higher order thinking and academic achievement among vocational high school students

The requirements of a contemporary workplace include the ability to think critically and creatively in order to solve problems and respond to changes in economic and social conditions. Unfortunately, vocational education often fails to prepare graduates for this environment due to limited resources, low student motivation, or the reliance upon outdated instructional strategies. The use of digital game-based learning (DGBL) for vocational education has been proposed, but has yet to be effectively implemented, particularly in terms of the promotion of higher order thinking skills (HOTS). Data from 68 eleventh grade vocational high school students were evaluated after a quasi-experimental, 27 week intervention. Pretest and posttest results were evaluated by MANCOVA and demonstrated that the experimental group (blended DGBL incorporating integrative HOTS activities) outperformed the comparison group (technology enhanced learning) in terms of creative thinking, critical thinking, problem solving, and academic achievement, with significant improvements on all four measures. While technology-enhanced learning was effective in promoting academic achievement and creative thinking, the DGBL condition was deemed most effective in providing an authentic context for developing employment-related skills and knowledge. Based on these results, a blended approach for DGBL, which incorporates instructor orchestration and scaffolding, provision of learning aids, and the use of collaborative learning, is recommended, particularly for vocational learners. This paper provides examples of a concrete model of DGBL instruction that was verified empirically as successful in significantly improving all three higher order thinking skills, including creative thinking, critical thinking, and problem solving.     

Effects of spatial ability and richness of motion cue on learning in mechanically complex domain

This study seeks to examine the impact of individual differences in the spatial ability of learners to integrate verbal information and three modes of visual representations. Several hypotheses were tested, including that (1) individual difference in spatial ability should influence the learning of theoretical knowledge when the instructional materials present a static visual representation at the lowest motion cue richness, and (2) both animations and the static visual representation containing motion cues should be more effective than static visual representation, especially for learners with low spatial ability. In the experiment, 60 learners were classified as having either low or high spatial ability on the basis of their performance on the Kit of Factor Referenced Cognitive Tests. The learners got knowledge from written explanations describing a four-stroke engine mechanism in a computer-based format. Also, written explanations were reinforced by corresponding visual representations with three levels of motion cue richness (static images, static images with motion cues or animations). Understanding was measured by a problem-solving transfer test. The results indicate that (1) presenting written explanations with corresponding animations did not improve performance of the learners with high spatial ability, (2) for the learners with low spatial ability, learning was enhanced by the use of animations, (3) merely adding motion cues to the static visual representation did not improve learning of the learners with low spatial ability, and (4) use of animations did not help learners with low spatial ability more than those with high spatial ability.