TOLA: Topic-oriented learning assistance based on ...

Future Generation Computer Systems (

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TOLA: Topic-oriented learning assistance based on cyber-physical system and big data Jeungeun Song a , Yin Zhang b , Kui Duan c,∗ , M. Shamim Hossain d , Sk Md Mizanur Rahman e a

School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

b

School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China

c

School Hospital, Huazhong University of Science and Technology, Wuhan, China

d

Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

e

Information Systems Department, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia

highlights • A big-data-driven approach named TOLA for online learning evolution is proposed to discover students’ learning pattern and guide courses improvement.

• A Data fusion of online learning data and threads data is proposed to extract topic of course for discovering the relationship between students and courses.

• A hybrid-feature-based classification is proposed for MOOC forums data for improve the accuracy of LDA-based classification. • Through experiment, it is proved that TOLA has good performance, which is expected to guide improving the quality of online education.

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Article history: Received 2 November 2015 Received in revised form 24 May 2016 Accepted 27 May 2016 Available online xxxx Keywords: Massive open online courses Big data Cyber-physical system Latent Dirichlet allocation Collaborative filtering

abstract Massive open online courses (MOOC) is a novel educational model emerging in recent years, which is assisted by advanced techniques such as cloud computing, big data and Cyber-Physical Systems (CPS). Through adequate analysis assisted by big data, the quality of education is expected to be extensively improved. Unfortunately, the MOOC data are not fully utilized for educational innovation, because the existing research focuses on the data generated in the online learning but neglects other related data, such as the forum data. In this article, we propose a big-data-driven approach named TOLA for online learning evolution to discover students’ learning pattern and guide courses improvement. Specifically, topic feature is extracted from MOOC forum through Latent Dirichlet Allocation, which is incorporated with other hybrid features. Through experiments, TOLA exhibits good performance in terms of complexity, efficiency and accuracy, facilitating the improvement of the quality of online education. © 2016 Elsevier B.V. All rights reserved.

1. Introduction With the development and popularization of smart devices, Information Technology (IT) has immensely enriched our daily life, and greatly affected our life style, specifically in healthcare, education and economic and other sectors. In recent years, Cyberphysical Systems (CPS) are emerging from the integration of em-

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Corresponding author. E-mail addresses: [email protected] (J. Song), [email protected] (Y. Zhang), [email protected] (K. Duan), [email protected] (M. Shamim Hossain), [email protected] (S.M.M. Rahman). http://dx.doi.org/10.1016/j.future.2016.05.040 0167-739X/© 2016 Elsevier B.V. All rights reserved.

bedded computing devices [1], smart objects [2,3], people and physical environments, which are typically tied by a communication infrastructure [4–6]. Assisted by CPS, one new model of teaching has quietly risen since 2012, i.e., Massive Open Online Courses (MOOC) which normally refer to massive open online network learning platforms. On such platforms, there are superior teaching resources provided by excellent colleges and universities around the world, enabling people to acquire knowledge anytime and anywhere as long as having the mobile devices or desktop computers accessed to network. Since MOOC system includes courses data collection, storage and feedback, it can be deemed as a CPS and has the following advantages compared to traditional education systems:

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• Resourceful: Through open platform, a large number of scholars

• Inadequacy: The threads data in MOOC include deep value,

publish their courses for free, especially the high-quality programs from famous universities, such as Corsera, Udacity, and edX including various excellent courses from Ivy League. • Interactive: MOOC provides more interactive communications between teachers and students, especially the interactive video services [7–10]. Assisted by CPS, classroom is not only a physical space, but also a cyber platform on MOOC. Teachers can publish their courses as video, and interact with students via forum or Social Networking Services (SNS). • Pervasive: Thanks to the explosion of mobile devices and wireless networks [11,12], the resource on MOOC can be accessed through many ways, which is an evolution in education to break through the limitations of time and space.

which can improve the quality of online course and learning efficiency. Unfortunately, existing researches mainly focus on click data rather than threads data.

Despite the rapid growth of MOOC, researches on MOOC are still not enough since there is still misunderstanding in cognition and some realistic problems need to be solved. Though MOOC is an evolution of educational method, it may not necessarily improve the learning experience than traditional education systems. For the students with high self-motivation, strong ability of independent study or high enthusiasm, MOOC can indeed provide more opportunities and choices. However, the students with relative weak ability or insufficient enthusiasm cannot benefit from such novel education model. Fortunately, the learning efficiency of students can be extensively improved by big data techniques [13,14], especially in the following aspects:

• Verification: The data about students learning collected in MOOC are mainly video clickstream interactions, and we are not sure that such click data represent the confirmation that students truly obtain knowledge. • Discovery: MOOC data provide an opportunity to analyze students learning characteristics and personal abilities, which can help us discover the relationship between the utilization of online courses and students achievements. • Evolution: From MOOC forum, the topic of course can be extracted from threads, which is useful to match students’ interests with the course. According to the different types of data, the MOOC data analysis includes the following direction:

• Behavior-based: In [15], Brinton et al. proposed prediction for the accuracy of student’s first answering through video click-stream data. In [16], Jermann et al. quantified clickstream data to establish regression model for predicting the course participation degree of students. In [17], Anderson et al. proposed classification for individual learning pattern based on trace data of student learning. • Forum-based: Although there are a great number of researches on forum topic classification [18,19], the traditional topic model is not available for MOOC. In [20], Rossi et al. proposed a topic classification method based on the interactive information of students in the course forums. In [21], Brinton et al. proposed generate-model-based approach to analyze the causal relationship between learning activity and drop out rate. Although there have been various researches on MOOC, they had very little effect on guiding the education with big data [22]. Furthermore, the conventional data mining models cannot fully meet the requirements for improving the quality of online courses [23]. In the explosion of big data, there are the following challenges for MOOC data:

• Ineffectiveness: Although we can recognize learning pattern of students from MOOC data, it fails to effectively help teachers to improve the course, because MOOC data only consists of some simple and straightforward learning activities, such as click behavior.

To address these issues, this paper proposes a big data based approach to discover the learning patterns of students from MOOC data, which provides scientific basis for improving the quality of online course. More specifically, this paper makes the following contributions to MOOC data analysis:

• We propose data fusion of online learning data and threads data to extract topic of course for discovering the relationship between students and courses. • We propose a hybrid-feature-based classification for MOOC forums data for improving the accuracy of LDA-based classification. 2. Theories and methods 2.1. Topic model Belonging to the fields of machine learning and natural language processing, topic modeling aims to make machine be able to independently learn and understand the semantics in text, and extract the document topic. Topic models were developed from Vector Space Model (VSM), including Mixture of Unigram model, Latent Semantic Analysis (LSA), and probabilistic Latent Semantic Analysis (pLSA) model [24]. Nowadays, latent Dirichlet allocation (LDA) is widely used for topic model extraction [25]. In this paper, we extract topic features from MOOC forum via LDA topic model. 2.2. Classification algorithm Classification is a method to distinguish sample data from training data with label, i.e., how to label sample data according to labeled training data. The typical classification algorithms include Nearest Neighbor Algorithm, Naive Bayes Algorithm and Support Vector Machine algorithm, etc. [26] Compared with other classification algorithms, SVM algorithm demonstrates considerable performance in processing the dataset with noise and linear inseparable dataset. Moreover, SVM does not involve probability and statistics, simplifying the complication degree of problems. Hence, SVM is deployed for topic classification in TOLA. 2.3. Recommendation algorithm The core of the recommended algorithms is information filtering and information retrieval to extract item features and model users’ interest for recommending suitable contents to user. Collaborative Filtering (CF) is one of the most representative recommendation algorithms, and it calculates user similarity for recommendation, including user-based and item-based CF [27]. In TOLA, an improved CF involving LDA is proposed to recommend threads to user. 3. Topic classification of MOOC threads based on hybrid features In this article, we analyze the data of MOOC course forums and propose the topic classification method based on hybrid features to solve the contradiction between the massive topic content in MOOC forums and the limited open courses.

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Fig. 1. A thread on Corsera. Table 1 The characteristic property of the thread data. Content type

Attributes name

Data types

Definition

Thread

thread_id course_id sub_forum depth n_views n_votes content post_time user_id user_type

unsigned int unsigned int string unsigned int unsigned int int string unsigned int unsigned int string

Thread identifier Course identifier Category tag Depth of this thread as a tree structure Hits Votes Content Post time User identifier of the poster User role of the poster

Post

post_id thread_id course_id parent_id user_type n_votes post_time content

unsigned int unsigned int unsigned int unsigned int string int unsigned int string

Post identifier Identifier of the thread this post belongs to Course identifier If it is a comment, the value should be 0; otherwise, it should be the parent post identifier User role of the poster Votes Post time Content

3.1. MOOC topic modeling In MOOC, each course has a discussion forum, including threads and sub-forums. Some threads are posted in sub-forums while others are directly posted in the discussion forum. Generally, each thread includes at least one post, which may include many comments. We regard post and comment as message, which are the sources to extract topic features by LDA. In Fig. 1, it illustrates a thread in the forum of ‘‘The Data Scientists Toolbox’’ on Corsera.1 It can be considered that all the messages in a thread match the content of this thread, which means the user does not randomly post to the thread with unrelated content. In TOLA, all the messages in a thread are stored in a document. Through word segmentation and removing stop words, we can obtain the word vector from this document. After processing the forum data, the thread document set of the course is taken as training for LDA topic model. In this article, all of the data is collected in Corsera, and the format of collected original data is JSON (JavaScript Object Notation). In Table 1, the details of thread and post data are described.

1 https://www.coursera.org/.

Particularly, (1) thread title is also considered as the content of this thread. (2) The first post of each thread is always the content published by the thread poster and its ID must be the same as this thread’s ID. (3) Each post may also have comment which is also considered as one kind of the posts. 3.2. Feature extraction The feature extraction is one of the most important procedures for data mining, which aims to extract representative information from the original data. Generally, the extracted data have to be further processed such as sampling, weighting, normalization, outlier removing and feature combination, so that the final generated data can be used for model training. In this article, we focus on extract feature from one thread in MOOC forum, which usually includes one or more posts and comments. The contents of all the posts from a thread are integrated into a document for word segmentation and stop word removal by the open source project Lucene.2 Finally, a thread can

2 https://lucene.apache.org/core/.

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be represented by a series of unrepeated words, which is available for topic-based feature extraction. According to our analysis, the topic of a thread can be represented not only by its content but also by its hits, votes, comments, posts. Therefore, we propose a hybrid-features-based topic classification of a thread, including the following features:

• np : represents the number of posts, which shall be at least one (the content posted by the publisher).

• nv : represents the number of hits. • nu : represents the number of users (including students and professors) participating in this thread.

• nst : represents the number of professors participating in the posts.

Fig. 2. Relationship between subject number and complexity.

• nw : represents the number of words presented in all the posts. • ra : represents the proportion of anonymous posts in this thread. • ∥v∥2 : represents the secondary normal form of all the votes in  np 2 this thread, i.e. ∥v∥2 = i=1 |vi | . • tr : represents the relative value between the post time of this thread and the opening time of the course. After the extraction, we found that the range of some features is too large. Therefore, normalization is essential to reduce the time consumption of training and improve the accuracy of model. 4. Case study 4.1. Experimental data and experiment design In this article, the experiments are designed for verifying the performance of TOLA, especially the accuracy of topic-based thread classification. The hardware environment of our experiments is a computer with Intel Pentium Dual core CPU, 2.7 GHz dominant frequency, 8 Gb primary memory. The experimental data are randomly selected from Corsera, which are described in Table 2.

Fig. 3. Model training time with different topic numbers.

The experiment is designed with the following steps: 1. The thread data of these three courses are preprocessed respectively for extracting the non-text features mentioned in Section 3.2. 2. The threads are topic modeled by LDA respectively to generate topic matrix. 3. The extracted non-text features are incorporated into topic feature matrix as a hybrid feature matrix. 4. The experimental data are divided into training set and test set with the proportion of 4:1. And the training data are used to train with libsvm.3 5. Test set is used to evaluate the performance of TOLA, which are compared with other methods. Particularly, the evaluation standards include F1-score, precision and recall [28]. 4.2. Measurement In LDA, the number of topics affects the computation complexity and modeling speed. Usually, perplexity is used to evaluate the quality of topic modeling [29]. Perplexity is defined as the geometric average reciprocal of the likelihood of each word in the trained model. The lower the perplexity is, the better the topic model is, which means the number of topics is optimal. Based on the experiments with the selected three courses shown in Fig. 2, we found that a higher number of topics cause a lower perplexity, but when the number is higher than 80, the optimization is not obvious. However, a higher number of topics leads to a higher computation complexity, which is proved in the experiment shown in 3. According to the experiment shown in Figs. 2 and 3, it is obvious that the optimal number of topics is 80.

3 http://www.csie.ntu.edu.tw/cjlin/libsvm/.

Fig. 4. Comparison of different algorithms classification accuracy.

4.3. Analysis and evaluation In TOLA, we classify topics by SVM. In order to verify the performance of topic classification, we compare the experimental results of SVM with other classification algorithms, i.e. K-nearest neighbor algorithm (KNN) and Naive Bayesian algorithm. As shown in Fig. 4, the topic classification accuracy of SVM is higher than the others. Especially, we evaluate the precision, recall and F1 of hybrid features classification of ‘‘Machine Learning’’ based on TOLA, which are compared with the conventional topic classification based on LDA. Table 3 describes the experiment result. As shown in the experiment result, we found that the more the training samples are, the better the performance of the classification is. Furthermore, the performance of hybrid-featurebased classification is better than LDA-based classification. 5. Conclusion In this article, we propose a big-data-driven approach for online learning evolution to discover students’ learning pattern and guide courses improvement. Specifically, hybrid topic feature is

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Table 2 The details of experimental data. Course title

Course identifier

Number of threads

Number of categories

Machine Learning Creative Programming for Digital Media Statistics: Making Sense of Data

ml-003 digitalmedia-001 Introstats-001

5188 2406 1131

9 9 7

Table 3 The result of topic classification of Machine Learning. Topic classes

Discussion Groups Technical Feedback Exercises Material Feedback Lectures Quizzes Materials Errors Technical Issues

Number of posts

1521 162 174 1877 312 932 177 20 13

F1

Precision

Recall

TOLA

LDA

TOLA

LDA

TOLA

LDA

85.8 81.6 82.0 89.3 80.0 89.8 75.1 68.2 72.2

47.2 48.3 56.3 69.8 57.0 74.2 57.7 42.5 40.0

88.1 79.6 85.1 92.6 77.9 89.0 78.4 62.3 66.6

84.3 62.1 65.8 85.1 69.5 88.7 66.9 47.6 50.0

83.7 83.7 79.2 86.3 82.2 90.6 71.9 75.5 78.8

32.8 38.9 49.2 59.2 48.3 63.8 50.7 38.4 33.4

extracted from MOOC forum, which is used for topic classification. Through experiment, it is proved that TOLA has good performance, which is expected to guide improving the quality of online education. Based on TOLA, we expect to automatically classify the threads in MOOC course forum so that professors can make targeted comments while students can find desirable content quickly. TOLA in this article refers to the offline modeling of historical data within a period. For future work, we will investigate how to process the individual learning data in an online manner and update the existing model dynamically. Acknowledgment The authors extend their appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia for funding this work through the research group Project No. RGP228. References [1] Giancarlo Fortino, Antonio Guerrieri, Wilma Russo, Agent-oriented smart objects development, in: 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design, CSCWD, 2012, pp. 907–912. [2] Giancarlo Fortino, Antonio Guerrieri, Wilma Russo, Claudio Savaglio, Middlewares for smart objects and smart environments: Overview and comparison, in: Internet of Things Based on Smart Objects, Technology, Middleware and Applications, 2014, pp. 1–27. [3] Giancarlo Fortino, Antonio Guerrieri, Michelangelo Lacopo, Matteo Lucia, Wilma Russo, An agent-based middleware for cooperating smart objects, in: PAAMS (Workshops), 2013, pp. 387–398. [4] D. Miorandi, S. Sicari, F. De Pellegrini, I. Chlamtac, Internet of things: Vision, applications and research challenges, Ad Hoc Netw. 10 (7) (2012) 1497–1516. [5] M. Chen, Y. Ma, Y. Hao, Y. Li, D. Wu, Y. Zhang, E. Song, CP-Robot: cloudassisted pillow robot for emotion sensing and interaction, in: Industrialiot 2016, Guangzhou, China, Mar. 2016. [6] M. Chen, Y. Hao, Y. Li, C. Lai, D. Wu, On the computation offloading at ad hoc cloudlet: Architecture and service models, IEEE Commun. 53 (6) (2015) 18–24. [7] T. Xu, W. Xiang, Q. Guo, L. Mo, Mining cloud 3D video data for interactive video services, Mobile Netw. Appl. 20 (3) (2015) 320–327. [8] M. Chen, Y. Zhang, Y. Li, S. Mao, V. Leung, EMC: Emotion-aware mobile cloud computing, IEEE Netw. 29 (2) (2015). [9] Giancarlo Fortino, Wilma Russo, Carlo Mastroianni, Carlos E. Palau, Manuel Esteve, CDN-supported collaborative media streaming control, IEEE MultiMedia 14 (2) (2007) 60–71. [10] M. Chen, J. Wang, K. Lin, D. Wu, J. Wan, L. Peng, C. Youn, Multipath planning based transmissions for IoT multimedia sensing, in: IEEE IWCMC, Greece, Sep. 2016.

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J. Song et al. / Future Generation Computer Systems ( Rachel ‘‘Jeungeun’’ Song is a Ph.D. candidate in School of Computer Science and Technology at Huazhong University of Science and Technology (HUST). Her research focuses on Internet of Things, Mobile Cloud, Body Area Networks, Emotion-aware Computing, Healthcare Big Data, CyberPhysical Systems, and Robotics, etc.

Yin Zhang is a faculty member of the School of Information and Safety Engineering, Zhongnan University of Economics and Law. He was a Poster-Doctoral Fellow in the School of Computer Science and Technology at Huazhong University of Science and Technology (HUST). He is a handling Guest Editor for New Review of Hypermedia and Multimedia and IEEE Sensors Journal. He serves as reviewer for IEEE Network, Information Sciences, etc. He is TPC Co-Chair for 6th International Conference on Cloud Computing (CloudComp 2015). He is Local Chair for the 9th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities (TRIDENTCOM 2014).

Kui Duan is an associate chief physician in School Hospital of Huazhong University of Science and Technology, Wuhan China. Her research interests include medical image processing and medical image analysis.

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M. Shamim Hossain is an Associate Professor at the King Saud University, Riyadh, KSA. Dr. Shamim Hossain received his Ph.D. in Electrical and Computer Engineering from the University of Ottawa, Canada. His research interests include serious games, cloud and multimedia for healthcare, resource provisioning for big data processing on media clouds and biologically inspired approach for multimedia and software system. He has authored and co-authored around 100 publications including refereed IEEE/ACM/Springer/Elsevier journals, conference papers, books, and book chapters. He has served as a member of the organizing and technical committees of several international conferences and workshops. He has served as co-chair, general chair, workshop chair, publication chair, and TPC for over 12 IEEE and ACM conferences and workshops. Currently, he serves as a co-chair of the 6th IEEE ICME workshop on Multimedia Services and Tools for E-health MUST-EH 2016. He is on the editorial board of International Journal of Multimedia Tools and Applications. Previously, he served as a guest editor of IEEE Transactions on Information Technology in Biomedicine (currently IEEE JBHI), Springer Multimedia tools and Applications (MTAP), Springer Cluster Computing and International Journal of Distributed Sensor Networks. Currently, he serves as a lead guest editor of IEEE Transactions on Cloud Computing, IEEE Communication Magazine, Future Generation Computer Systems (Elsevier), SENSORS (MDPI), and Computers & Electrical Engineering (Elsevier). Dr. Shamim is a Senior Member of IEEE, a member of ACM and ACM SIGMM. Sk Md Mizanur Rahman is an Assistant Professor in Information Systems Department in the College of Computer and Information Sciences at King Saud University, KSA. Prior to his current appointment, he worked for several years in cryptography and security engineering in the high-tech industry in Ottawa, Canada. He also worked as a postdoctoral researcher for several years in University of Ottawa, University of Ontario Institute of Technology (UOIT), and University of Guelph, Canada. He completed a Ph.D. in Risk Engineering (Major: Cyber Security Engineering) in the Laboratory of Cryptography and Information Security, Department of Risk Engineering, University of Tsukuba, Japan, on March 2007. Information Processing Society Japan (IPSJ) awarded Dr. Rahman with ‘‘IPSJ Digital Courier Funai Young Researcher Encouragement Award’’ for his excellent contribution in IT security research. He was awarded ‘‘Gold Medal’’ for the distinction marks in his undergraduate and graduate programs. Primary research interest of Dr. Rahman is on Cryptography, Software Security, Information Security, Privacy Enhancing Technology and Network Security. He has published over 60 peerreviewed journal and international conference research papers and book chapters.