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Deeva G, De Smedt J, Saint-Pierre C, Weber R, De Weerdt J. Predicting student performance using sequence classification with time-based windows. EXPERT SYSTEMS WITH APPLICATIONS 2022; 209:118182. [PMID: 35966368 PMCID: PMC9359516 DOI: 10.1016/j.eswa.2022.118182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 05/24/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learning experience and widening their educational prospects, but also an opportunity to gain insights into students' learning processes with learning analytics. This study contributes to the topic of improving and understanding e-learning processes in the following ways. First, we demonstrate that accurate predictive models can be built based on sequential patterns derived from students' behavioral data, which are able to identify underperforming students early in the course. Second, we investigate the specificity-generalizability trade-off in building such predictive models by investigating whether predictive models should be built for every course individually based on course-specific sequential patterns, or across several courses based on more general behavioral patterns. Finally, we present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable to predict student performance with high levels of accuracy, reaching 90% for course-specific models.
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Affiliation(s)
- Galina Deeva
- Research Centre for Information Systems Engineering, KU Leuven, Belgium
| | - Johannes De Smedt
- Research Centre for Information Systems Engineering, KU Leuven, Belgium
| | | | - Richard Weber
- Department of Industrial Engineering, FCFM, Instituto Sistemas Complejos de Ingeniería, Universidad de Chile, Chile
| | - Jochen De Weerdt
- Research Centre for Information Systems Engineering, KU Leuven, Belgium
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Amirat H, Lagraa N, Fournier-Viger P, Ouinten Y, Kherfi ML, Guellouma Y. Incremental tree-based successive POI recommendation in location-based social networks. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03842-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Kaushik K, Reddy PK, Mondal A, Ralla A. An incremental framework to extract coverage patterns for dynamic databases. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2021. [DOI: 10.1007/s41060-021-00262-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Zhou S, Liu H, Chen B, Hou W, Ji X, Zhang Y, Chang W, Xiao Y. Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns. ENTROPY 2021; 23:e23060738. [PMID: 34208012 PMCID: PMC8230706 DOI: 10.3390/e23060738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 11/16/2022]
Abstract
The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.
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MpBsmi: A new algorithm for the recognition of continuous biological sequence pattern based on index structure. PLoS One 2018; 13:e0195601. [PMID: 29684052 PMCID: PMC5912758 DOI: 10.1371/journal.pone.0195601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Accepted: 03/26/2018] [Indexed: 12/05/2022] Open
Abstract
A significant approach for the discovery of biological regulatory rules of genes, protein and their inheritance relationships is the extraction of meaningful patterns from biological sequence data. The existing algorithms of sequence pattern discovery, like MSPM and FBSB, suffice their low efficiency and accuracy. In order to deal with this issue, this paper presents a new algorithm for biological sequence pattern mining abbreviated MpBsmi based on the data index structure. The MpBsmi algorithm employs a sequence position table abbreviated ST and a sequence database index structure named DB-Index for data storing, mining and pattern expansion. The ST and DB-Index of single items are firstly obtained through scanning sequence database once. Then a new algorithm for fast support counting is developed to mine the table ST to identify the frequent single items. Based on a connection strategy, the frequent patterns are expanded and the expanded table ST is updated by scanning the DB-Index. The fast support counting algorithm is used for obtaining the frequent expansion patterns. Finally, a new pruning technique is developed for extended pattern to avoid the generation of unnecessarily large number of candidate patterns. The experiments results on multiple classical protein sequences from the Pfam database validate the performance of the proposed algorithm including the accuracy, stability and scalability. It is showed that the proposed algorithm has achieved the better space efficiency, stability and scalability comparing with MSPM, FBSB which are the two main algorithms for biological sequence mining.
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Hui L, Chen YC, Weng JTY, Lee SY. Incremental mining of temporal patterns in interval-based database. Knowl Inf Syst 2015. [DOI: 10.1007/s10115-015-0828-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Huang CK, Chang TY, Narayanan BG. Mining the change of customer behavior in dynamic markets. INFORMATION TECHNOLOGY & MANAGEMENT 2014. [DOI: 10.1007/s10799-014-0197-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Subramanyam RBV, Suresh Rao A, Karnati R, Suvvari S, Somayajulu DVLN. Mining Closed Sequential Patterns in Progressive Databases. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2013. [DOI: 10.1142/s021964921350024x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Previous studies of Mining Closed Sequential Patterns suggested several heuristics and proposed some computationally effective techniques. Like, Bidirectional Extension with closure checking schemas, Back scan search space pruning, and scan skip optimization used in BIDE (BI-Directional Extension) algorithm. Many researchers were inspired with the efficiency of BIDE, have tried to apply the technique implied by BIDE to various kinds of databases; we toofelt that it can be applied over progressive databases. Without tailoring BIDE, it cannot be applied to dynamic databases. The concept of progressive databases explores the nature of incremental databases by defining the parameters like, Period of Interest (POI), user defined minimum support. An algorithm PISA (Progressive mIning Sequential pAttern mining) was proposed by Huang et al. for finding all sequential patterns over progressive databases. The structure of PISA helps in space utilization by limiting the height of the tree, to the length of POI and this issue is also a motivation for further improvement in this work. In this paper, a tree structure LCT (Label, Customer-id, and Time stamp) is proposed, and an approach formining closed sequential patterns using closure checking schemas across the progressive databases concept. The significance of LCT structure is, confining its height to a maximum of two levels. The algorithmic approach describes that the window size can be increased by one unit of time. The complexity of the proposed algorithmic approach is also analysed. The approach is validated using synthetic data sets available in Internet and shows a better performance in comparison to the existing methods.
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Affiliation(s)
- R. B. V. Subramanyam
- Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
| | - A. Suresh Rao
- Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
| | - Ramesh Karnati
- Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
| | - Somaraju Suvvari
- Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
| | - D. V. L. N. Somayajulu
- Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
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Mallick B, Garg D, Grover PS. Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database. INT J COMPUT INT SYS 2013. [DOI: 10.1080/18756891.2013.768432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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An approach to products placement in supermarkets using PrefixSpan algorithm. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2013. [DOI: 10.1016/j.jksuci.2012.07.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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DRL-Prefixspan: A novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns. OPEN COMPUTER SCIENCE 2012. [DOI: 10.2478/s13537-012-0030-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
AbstractDiscovering sequential patterns is a rather well-studied area in data mining and has been found many diverse applications, such as basket analysis, telecommunications, etc. In this article, we propose an efficient algorithm that incorporates constraints and promotion-based marketing scenarios for the mining of valuable sequential patterns. Incorporating specific constraints into the sequential mining process has enabled the discovery of more user-centered patterns. We move one step ahead and integrate three significant marketing scenarios for mining promotion-oriented sequential patterns. The promotion-based market scenarios considered in the proposed research are 1) product Downturn, 2) product Revision and 3) product Launch (DRL). Each of these scenarios is characterized by distinct item and adjacency constraints. We have developed a novel DRL-PrefixSpan algorithm (tailored form of the PrefixSpan) for mining all length DRL patterns. The proposed algorithm has been validated on synthetic sequential databases. The experimental results demonstrate the effectiveness of incorporating the promotion-based marketing scenarios in the sequential pattern mining process.
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SUBRAMANYAM RBV, GOSWAMI A. A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS. INT J UNCERTAIN FUZZ 2011. [DOI: 10.1142/s0218488505003722] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.
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Affiliation(s)
| | - A. GOSWAMI
- Indian Institute of Technology, Kharagpur, India
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Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events. DATA KNOWL ENG 2009. [DOI: 10.1016/j.datak.2009.06.010] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Hamrouni T, Ben Yahia S, Mephu Nguifo E. Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets. DATA KNOWL ENG 2009. [DOI: 10.1016/j.datak.2009.05.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lee AJ, Wu HW, Lee TY, Liu YH, Chen KT. Mining closed patterns in multi-sequence time-series databases. DATA KNOWL ENG 2009. [DOI: 10.1016/j.datak.2009.04.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ezeife CI, Liu Y. Fast incremental mining of web sequential patterns with PLWAP tree. Data Min Knowl Discov 2009. [DOI: 10.1007/s10618-009-0133-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Chang L, Wang T, Yang D, Luan H, Tang S. Efficient algorithms for incremental maintenance of closed sequential patterns in large databases. DATA KNOWL ENG 2009. [DOI: 10.1016/j.datak.2008.08.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Lee AJ, Wang CS, Weng WY, Chen YA, Wu HW. An efficient algorithm for mining closed inter-transaction itemsets. DATA KNOWL ENG 2008. [DOI: 10.1016/j.datak.2008.02.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Kim SW, Park S, Won JI, Kim SW. Privacy preserving data mining of sequential patterns for network traffic data. Inf Sci (N Y) 2008. [DOI: 10.1016/j.ins.2007.08.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Nguyen SN, Sun X, Orlowska ME. Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING 2005. [DOI: 10.1007/11430919_52] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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