Digital resources for nursing education:Open courseware and massive open online courses

Open Courseware(OCW)and massive open online courses(MOOCs)are teaching and learning resources that are easily accessible to anyone with an Internet connection.OCW is digitally published learning content including full and partial courses(syllabi,outlines,lectures in pdf or video,slides,reference lists,etc.),simulations,animations,tutorials,drills and practices,modules,podcasts,case studies,and quizzes.This content is free and can be adopted or adapted to the user's needs.MOOCs are online learning experiences taught by university professors using conventional educational tools including video lectures,interactive modules,assignments,study materials,discussion boards,quizzes and tests.MOOCs are offered free or at low cost for personal and professional learning,and as a supplement to classroom teaching.Many MOOCs and OCW focus on topics of interest to nursing,particularly to nurse educators.This article provides the reader with a brief history of the development of OCW and MOOCs,conceptual descriptions,and guidance about how to access and use these new online resources.

An Ensemble Learning Model for Early Dropout Prediction of MOOC Courses

Massive open online courses(MOOCs)have become a way of online learning across the world in the past few years.However,the extremely high dropout rate has brought many challenges to the development of online learning.Most of the current methods have low accuracy and poor generalization ability when dealing with high-dimensional dropout features.They focus on the analysis of the learning score and check result of online course,but neglect the phased student behaviors.Besides,the status of student participation at a given moment is necessarily impacted by the prior status of learning.To address these issues,this paper has proposed an ensemble learning model for early dropout prediction(ELM-EDP)that integrates attention-based document representation as a vector(A-Doc2vec),feature learning of course difficulty,and weighted soft voting ensemble with heterogeneous classifiers(WSV-HC).First,A-Doc2vec is proposed to learn sequence features of student behaviors of watching lecture videos and completing course assignments.It also captures the relationship between courses and videos.Then,a feature learning method is proposed to reduce the interference caused by the differences of course difficulty on the dropout prediction.Finally,WSV-HC is proposed to highlight the benefits of integration strategies of boosting and bagging.Experiments on the MOOCCube2020 dataset show that the high accuracy of our ELM-EDP has better results on Accuracy,Precision,Recall,and F1.

A Meta-Synthesis Study of MOOC Literature on Students' Learning

This research is a meta-synthesis study of Massive online open course(MOOC) literature on students' learning.It aims to investigate key components for the design of a MOOC as well as the benefits and challenged engaged in MOOCs' learning.It is found that in MOOC's all stages,discussion forum,video features and instructor's teaching are well connected to each other.

智慧教育背景下SPOC混合教学中教师角色的转变

大数据、物联网、人工智能等新一代信息技术与教育教学的深度融合使教学方式与学习方式更加智慧化,而智慧教育生态系统的核心之一是教师的优质支持服务,这对教师发展与能力素养提出了新的要求和挑战。SPOC混合教学中教师作为课程的提供者与主导者,需要具有先进的教育理念、良好的数字素养、终身学习能力等。本文主要以智慧教育为背景,主要从在线上与线下混合培训、学习的角度探究SPOC混合教学中教师的角色转变。以期为智慧教育背景下教师数字素养能力发展提供理论参考。

Constructing an Educational Knowledge Graph with Concepts Linked to Wikipedia

To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.This paper demonstrates the whole process of educational knowledge graph construction for reference.And this knowledge graph,the largest knowledge graph of MOOC resources at present,stores and represents five classes,11 kinds of relations and 52779 entities with their corresponding properties,amounting to more than 300000 triples.Notably,24188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically,which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links.Besides,prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph.This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications,such as prerequisites mining.

Consideration of the Local Correlation of Learning Behaviors to Predict Dropouts from MOOCs

Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.