教育数据挖掘研究进展综述

教育数据挖掘(educational data mining,简称EDM)技术运用教育学、计算机科学、心理学和统计学等多个学科的理论和技术来解决教育研究与教学实践中的问题.在大数据时代背景下,EDM研究将迎来新的转折点.为方便读者了解EDM的研究进展或从事相关研究和实践,首先介绍EDM研究的概貌、特点和发展历程,然后重点介绍和分析了EDM近年来的研究成果.在成果介绍部分,选取的研究成果大部分发表于2013年以后,包括以往较少涉及的几种新型教育技术.在成果分析部分,对近年来的典型案例作了分类、统计和对比分析,对EDM研究的特点、不足及发展趋势进行了归纳和预测.最后讨论了大数据时代下EDM面临的机遇和挑战.     

高校教育信息化中数据挖掘的应用研究

数据挖掘作为一种高效的数据处理技术,被广泛的应用到各个领域。高校教育信息化的快速发展为高校积累了大量的信息,利用数据挖掘技术可以发现在数据中隐藏的普遍规律和模式,为高校教育教学工作的顺利开展提供了科学的依据。本文通过对数据挖掘概念、过程及进行数据挖掘必要性的分析,探讨数据挖掘技术在高校教育信息化中的应用问题。     

数据挖掘技术在教育行业中的应用探讨

文章对数据挖掘的基本概念做了介绍,并对其在教育行业的应用做了详细的分析,从学生特征分析、师生行为干预、课程设置和学习评价四个方面理论联系实际地阐述了数据挖掘技术可以为教育过程提供哪些帮助。     

数据挖掘技术在教育领域中的作用

在对数据挖掘技术的概念、挖掘过程、和主要功能等知识进行简单介绍的基础上，探讨了其在成绩分析、智能题库、教育评价、教育管理等方面的应用。     

数据挖掘在教育教学中的应用综述

随着教育信息化的不断发展,基于数据库技术的数据挖掘与教育教学的联系也越来越紧密。通过对国内外数据挖掘在教育教学领域中的应用研究相关文献进行分析,从数据挖掘相关技术的角度出发,总结并归纳了各自在该领域中的应用和研究现状。最后提出了数据挖掘在该领域研究中存在的一些问题与难题以及发展前景。     

数据挖掘技术在教育中的应用探析

处在网络信息技术高速发展的大背景下,教育发展趋势也呈现出信息化的特点,教育数据资源种类繁多,数量庞大,如何有效的对这些数据进行利用和管理成为当前高校教育面临的主要任务,数据挖掘技术有效的解决了这一问题。本文主要对数据挖掘的概念和技术进行了分析,并总结了数据挖掘技术在教育中的应用,以期提高教学效果,更好的指导教育活动的开展,为相关研究提供参考意见。     

一种基于Web服务的分布式数据挖掘体系结构

分布式数据挖掘是数据挖掘领域的一个新兴研究课题，而其主要问题是知识共享和软组件重用。结合Web服务技术的跨平台、统一数据表示格式以及可实现软组件重用和数据重用等优点，文中提出了一种基于Web服务的分布式数据挖掘体系，可实现分布式异构环境下的大容量数据的数据挖掘．旨在对异构数据库的数据挖掘进行一些有意义的探讨。     

Moodle平台数据挖掘方法设计与实现

教育数据挖掘是一个新兴的研究方向。如何把存储在教育软件系统中的数据转变为有意义的信息,并为教育决策、优化教学过程服务,已成为大多数教育工作者所关注的内容。文中总结了当前教育数据挖掘的研究现状,介绍了一种基于Excel的简单数据挖掘方法。该方法利用模糊C均值聚类算法,对Moodle平台积累的日志数据进行分析,找出有相似学习行为的学生,为学习社区的小组划分和研究学习模式服务。实验表明,该方法能够更准确地对学生进行分类,而且操作更为简单、方便。     

数据挖掘技术在高校管理中的应用

本文介绍了数据挖掘的概念、几种知识模型和主要技术方法，分析数据挖掘技术在高校教育教学中的应用和存在问题。     

数据挖掘技术在教育中的应用研究

随着教育信息化进程的推进，产生并积累了大量的、复杂的数据，为了更充分、有效地利用这些数据，本文就数据挖掘技术在教育中的应用进行了一些探讨，并以教学评价作为简单的实例研究，目的在于发现大量教育数据中隐藏的、有用的知识，以指导教育、发展教育、为教育服务。     

Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

基于互信息的教育数据矩阵加权正负关联模式发现

本文将互信息模型引入教育数据关联模式挖掘,提出一种基于互信息的教育数据矩阵加权正负关联模式挖掘算法,给出与其相关的定理及其证明。本文算法克服了现有挖掘算法的缺陷,考虑了教育数据项集在学生信息数据库中具有的权值,采用新的正负关联模式评价标准,挖掘出更接近实际情况的正负关联模式。通过关联模式分析,发现教育数据中潜在有用的教育、教学规律和教育发展趋势,为教育管理、教育决策和教学改革提供科学的依据。以真实的教育数据作为实验数据测试集,实验结果表明,本文算法有效,在教育信息化数据处理与分析中具有重要的应用价值。     

Assessing implicit science learning in digital games

Building on the promise shown in game-based learning research, this paper explores methods for Game-Based Learning Assessments (GBLA) using a variety of educational data mining techniques (EDM). GBLA research examines patterns of behaviors evident in game data logs for the measurement of implicit learning—the development of unarticulated knowledge that is not yet expressible on a test or formal assessment. This paper reports on the study of two digital games showing how the combination of human coding with EDM has enabled researchers to measure implicit learning of Physics. In the game Impulse, researchers combined human coding of video with educational data mining to create a set of automated detectors of students' implicit understanding of Newtonian mechanics. For Quantum Spectre, an optics puzzle game, human coding of Interaction Networks was used to identify common student errors. Findings show that several of our measures of student implicit learning within these games were significantly correlated with improvements in external postassessments. Methods and detailed findings were different for each type of game. These results suggest GBLA shows promise for future work such as adaptive games and in-class, data-driven formative assessments, but design of the assessment mechanics must be carefully crafted for each game.     

Short Text Mining for Classifying Educational Objectives and Outcomes

Most of the international accreditation bodies in engineering education (e.g., ABET) and outcome-based educational systems have based their assessments on learning outcomes and program educational objectives. However, mapping program educational objectives (PEOs) to student outcomes (SOs) is a challenging and time-consuming task, especially for a new program which is applying for ABET-EAC (American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission) accreditation. In addition, ABET needs to automatically ensure that the mapping (classification) is reasonable and correct. The classification also plays a vital role in the assessment of students’ learning. Since the PEOs are expressed as short text, they do not contain enough semantic meaning and information, and consequently they suffer from high sparseness, multidimensionality and the curse of dimensionality. In this work, a novel associative short text classification technique is proposed to map PEOs to SOs. The datasets are extracted from 152 self-study reports (SSRs) that were produced in operational settings in an engineering program accredited by ABET-EAC. The datasets are processed and transformed into a representational form appropriate for association rule mining. The extracted rules are utilized as delegate classifiers to map PEOs to SOs. The proposed associative classification of the mapping of PEOs to SOs has shown promising results, which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.     

Sentiment mining in a collaborative learning environment: capitalising on big data

ABSTRACT

The ability to exploit students’ sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students’ data using machine learning techniques, and (ii) modelling and predicting students’ emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders.     

User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments

Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.     

数据挖掘技术在成才因素分析中的应用研究

如何在高校有效地开展素质教育是一个迫切的问题,而通过对高校学生数据库的挖掘,有可能在这方面获得一些科学依据。文中介绍了数据仓库及数据挖掘基本技术,并将数据挖掘技术应用到成才因素分析之上,通过大量的实际数据测试,获取了感兴趣度较大的规则模式,在高校素质教育的具体应用中起到了促进的作用。最后给出了一个基于MSAnalysis Server的多维数据集及数据挖掘结果分析可视化平台。     

数据挖掘中孤立点的分析研究在实践中应用

介绍了孤立点的定义和三种挖掘算法,即基于统计的方法、基于距离的方法和基于偏离的方法，在这个基础上,尝试了利用孤立点检测方法对教务管理系统中积累的数据进行分析,并验证了基于距离和的孤立点检测算法的有效性，通过实验,结果分析表明：基于距离和的算法降低了检测过程对用户设置阈值的要求,在时间复杂度上,稍微优于循环嵌套算法。