Mining temporal characteristics of behaviors from interval events in e-learning☆
Introduction
Information technology has changed the way in which people live and work. In addition, it has a significant influence on the educational domain. Currently, e-learning plays an increasingly important role. Most e-learning systems are capable of keeping detailed logs of user interactions, including keyboard clicking, eye tracking, and video browsing. These data create new opportunities for learning how students behave.
As an operation occurs, an e-learning system instantly records the corresponding interactive event. An event corresponds to a specified event type, which usually has a starting point, an end point, and a list of attributes that describe the event [39]. Educators may need to find the temporal characteristics of individuals’ behaviors to gain further insight into their learning habits, preferences, and cognitive efforts over time [4]. However, this task is not easy to accomplish, as we typically are not able to obtain obvious cues from massive and fragmented events. These cues include detecting important events (IEs) and their temporal relations. These IEs and relations are both desired because the former represent particular preferences and habits, while the latter represent certain causal associations or temporal patterns. This paper aims to provide knowledge to system designers, teachers, leaders, and students that enables them to understand how individuals behave over time; moreover, it seeks to provide, for the first time, evidence supporting the promotion of certain IEs and temporal relations in human-computer interaction design.
The temporal characteristics characterize not only when and what type of behavior a student engages in but also cases in which behaviors change. A simple example is the case of video-viewing behavior [7], [8]. The authors used limited video clickstream events such as play and pause. Thus, the temporal characteristics were easy to obtain, as the play event indicates the start of a cognitive activity, while the stop event indicates the termination of an activity. The temporal characteristics may simply display play-stop loops or something similar to play-play-stop with different durations. One learns that a student stops watching after three seconds or is probably searching for something as he triggers multiple consecutive play events. Moreover, one learns that a student permanently stops watching a video because of disinterest when seven loops occur in succession. In a more complex example, we assume that there are many more events than those considered in the above scenario. Suppose a student browses objects (maybe a video, a PPT, or a structured site) in parallel, as in Fig. 1. He may switch between them, browse multiple times through different parts of one object intermittently, or leave for non-overlapping and uneven temporal durations due to various cognitive demands. Is it possible to address the temporal characteristics of this complex scenario? In other words, can we determine whether an event is important, what the temporal pattern is, and how to characterize it? Unfortunately, we have not found a direct and effective approach to answer these questions.
The traditional approaches treat the groups of consecutive events as time-ordered sequences and discover frequent patterns and association rules using the sequential pattern mining technique [26]. They aim to find those item sets whose occurrences exceed a pair of user-defined support and confidence thresholds. However, in e-learning domains, people may be interested in discovering not only frequent events but also meaningful events, i.e., IEs. In this case, using the foregoing methods may not lead to satisfactory results, as one cannot judge an event’s significance based on the frequency of individual operations. Rather, the duration of these operations may be useful. On the one hand, some events with a low frequency and long duration may be more valuable than those with high frequency and short duration, as they reflect the effectiveness and significance of learning activities to some extent. For example, in the guided example, B is practically more valuable than C, even if the occurrence of C is apparently higher than that of B. Another fact is that we cannot simply ignore frequent events of short duration, which we refer to as tiny-interval events, because we have no idea whether they are pedagogically meaningless or fragments of IEs. Take C for example; we may also regard it as important despite its short duration.
Therefore, the focus of this paper is to explore a method for discovering temporal characteristics of interest from interval-based events with consideration of both event frequency and duration. To the best of our knowledge, most works on temporal data mining do not take into account event frequency and duration simultaneously. It is necessary to identify important events and exclude irrelevant events. Thus, events are first summarized according to the semantics and further segmented into equal-sized and non-overlapping pieces. The task of finding temporal characteristics is addressed by mining complex temporally frequent patterns and association rules. The major contributions are as follows:
- •
An evaluation method for effectively identifying events from large-scale event streams by taking both the frequency and duration into account is proposed.
- •
A complete mining framework for obtaining temporal characteristics of interest is proposed, and the educational implications of the results are analyzed.
- •
To evaluate the performance and practicability of the proposed methods, a series of extensive experiments are conducted on both synthetic and real datasets. The results reveal acceptable system overhead and satisfactory quality of patterns.
The remainder of the paper is organized as follows. Related works are discussed in Section 2. Section 3 introduces the preliminaries and research framework. An algorithm for efficiently processing interval-based temporal data is discussed in Section 4. In Section 5, algorithms for discovering the temporal characteristics of interest are presented. Section 6 demonstrates the performance and serviceability of the algorithms, presents the results of the experiments, and provides a discussion on interesting topics. Finally, conclusions are drawn in Section 7.
Section snippets
Educational temporal mining
Educational data mining (EDM), or learning analytics (LA), uses the statistics, machine learning and data mining techniques to analyze data that are generated from the interaction of teaching and learning to discover educational issues, better understand the states of students, and determine how students adapt to different contexts. As a new area of research, EDM has attracted more and more attention in recent years. The research can be roughly divided into three categories: 1) grouping such
Preliminaries
Definition 1 (Interactive event and interactive log). An interactive event e is caused by a user who interacts with the system. Let be a set of primitive time units and where s is the type of event and ti, i ∈ [1, n], is a timestamp marked when an event occurs. An interactive log is a set of interactive events logged chronologically. Definition 2 (Event type and event sequence). An event corresponds to a specified event type and explains the meaning behind a user’s operation. An event e during a
Evaluation of events
A subsequence may contain several event types. The question is how to discriminate the final types if the events are semantically relevant. In this work, this is carried out based on the relative importance of events instead of their frequency. The importance is a function of an event’s density and the relative intensity of the semantics conveyed over its duration. We use a formalism RS = F(d, r) to model their relationships, where d represents an event’s density and r denotes the relative
Mining temporal characteristics
In this section, we discover the temporal characteristics of individuals’ behaviors based on SETI and present an effective graph model, namely, a temporal-event graph (TEG). The temporal characteristics indicate not only when and what kind of behaviors a student performed but also the cases in which the behaviors would change. Therefore, a TEG is a graph showing both the IEs and temporal information. We use nodes to denote events of interest, directed edges to denote the evolution of events
Experimental evaluation
We conduct a series of extensive experiments on both synthetic and real datasets. Our goal is to evaluate the performance of the proposed framework and verify the serviceability in an e-learning environment. All the experiments are scripted using JAVA and performed on a 2.20 GHz machine with 4 GB of memory and the Windows 7 64-bit operating system.
Conclusions
Temporal data provide an alternative way to analyze student behaviors. Most temporal data mining algorithms mainly address the frequencies of events instead of their duration and their temporal indications. One might not find knowledge of interest when treating the times at which events occur as numerical values because the frequency of occurrence is a flawed indicator of the validity of events.
A framework was proposed in this paper to mine the temporal characteristics of individuals’ behaviors
References (51)
- et al.
G-Spamine: an approach to discover temporal association patterns and trends in internet of things
Fut. Generat. Comput. Syst.
(2017) - et al.
Using learning styles and viewing styles in streaming video
Comput. Edu.
(2011) - et al.
An efficient algorithm for incremental mining of temporal association rules
Data Knowl. Eng.
(2010) - et al.
Mining students behavior in web-based learning programs
Expert Syst. Appl.
(2009) - et al.
Mining temporal interval relational rules from temporal data
J. Syst. Softw.
(2009) - et al.
Beyond intratransaction association analysis: mining multidimensional intertransaction association rules
ACM Trans. Inf. Syst.
(2000) - et al.
Generalized k-means-based clustering for temporal data under weighted and kernel time warp
Pattern Recognit. Lett.
(2016) - et al.
Temporal bayesian networks
Proceedings of the TIME-94 – International Workshop on Temporal Representation and Reasoning
(1994) - et al.
Mining temporal association rules with frequent itemsets tree
Appl. Soft Comput.
(2017) - et al.
A behavioral sequence analyzing framework for grouping students in an e-learning system
Knowl. Based Syst.
(2016)
Similarity-profiled temporal association mining
IEEE Trans. Knowl. Data Eng.
New algorithms for fast discovery of association rules
KDD
Maintaining knowledge about temporal intervals
Commun. ACM
Anomalous event eetection in traffic video based on sequential temporal patterns of spatial interval events
KSII Trans. Internet Inf. Syst.
Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning
Metacogn. Learn.
Using log files from streaming media servers for optimising the learning sequence
Int. J.f Continu. Eng. Edu. Life Long Learn.
Mining mooc clickstreams: video-watching behavior vs. in-video quiz performance
IEEE Trans. Signal Process.
Mooc performance prediction via clickstream data and social learning networks
Computer Communications (INFOCOM), 2015 IEEE Conference on
An efficient algorithm for mining time interval-based patterns in large database
Proceedings of the 19th ACM International Conference on Information and Knowledge Management
Mining temporal patterns in time interva-based data
IEEE Trans. Knowl. Data Eng.
An algebra of granular temporal relations for qualitative reasoning
IJCAI
Semantics-based event log aggregation for process mining and analytics
Inf. Syst. Front.
A graph-based consensus maximization approach for combining multiple supervised and unsupervised models
IEEE Trans. Knowl. Data Eng.
The measurement of learners self-regulated cognitive and metacognitive processes while using computer-based learning environments
Edu. Psychol.
Efficient mining of partial periodic patterns in time series database
Data Engineering, 1999. Proceedings., 15th International Conference on
Cited by (13)
Event evolution model for cybersecurity event mining in tweet streams
2020, Information SciencesCitation Excerpt :The discovered author interest information, in turn, was proven to improve topic extraction accuracy. Topic evolution modeling can be extended to the temporal evolution of students’ behaviours in e-learning systems [32], with the linguistic structure in the system logs keeping user interactions. In this section, we first introduce the concept of critical domain relevant patterns, regarded as basic data points for clustering cybersecurity events.
Systematic Review and Analysis of EDM for Predicting the Academic Performance of Students
2024, Journal of The Institution of Engineers (India): Series BDiscovering Utility-driven Interval Rules
2023, arXivA survey on artificial intelligence techniques for security event correlation: models, challenges, and opportunities
2023, Artificial Intelligence ReviewA Review of Data Mining in Education Sector
2022, Journal of Engineering Education Transformations
- ☆
This research was partially supported by the MOE-China Mobile Research Fund Project No. MCM20160405, the Fundamental Research Fund for the Central Universities of MOE No. SWU118006, the MOE Innovation Research Team No. IRT13035, the Coordinator Innovation Project for the Key Lab of Shaanxi Province under Grant No. 2013SZS05-Z01, the Online Education Research Foundation of the MOE Research Center for Online Education under Grant Nos. 2016YB165 and 2016YB169, the Natural Science Basic Research Plan in Shaanxi Province of China Nos. 2016JM6027 and 2016JM6080, and the Project of China Knowledge Centre for Engineering Science and Technology.