Filters








463 Hits in 2.0 sec

The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

Rachel Baker, Di Xu, Jihyun Park, Renzhe Yu, Qiujie Li, Bianca Cung, Christian Fischer, Fernando Rodriguez, Mark Warschauer, Padhraic Smyth
2020 International Journal of Educational Technology in Higher Education  
In this paper, we provide an overview of the use of clickstream data to define and identify behavioral patterns that are related to student learning outcomes.  ...  Student clickstream data-time-stamped records of click events in online courses-can provide fine-grained information about student learning.  ...  These types of detected behavior changes were highly correlated with student outcomes.  ... 
doi:10.1186/s41239-020-00187-1 fatcat:5rzof6fxtbhq7pvayp5ctmkzsi

Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions [article]

Tanmay Sinha, Patrick Jermann, Nan Li, Pierre Dillenbourg
2014 arXiv   pre-print
As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which  ...  In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms.  ...  Operationalizing the Clickstream Level 1 (Operations) From our raw clickstream data, we construct a detailed encoding of students' clicks in the following 8 categories: Play (Pl), Pause (Pa), SeekFw  ... 
arXiv:1407.7131v2 fatcat:j3l6qnfu3bhqpksy674hf4u22m

Clickstream for learning analytics to assess students' behavior with Scratch

Daniel Amo Filvà, Marc Alier Forment, Francisco José García-Peñalvo, David Fonseca Escudero, María José Casañ
2019 Future generations computer systems  
Through collection and analysis of data generated by students' clicks in Scratch, we proceed to execute both exploratory and predictive analytics to detect patterns in students' behavior when developing  ...  Thus, we have developed a functional solution to categorize and understand students' behavior in programming activities based in Scratch.  ...  This type of behavior defines those students who make rapid changes in the program and constantly check the results. P O S T We could have proposed many more patterns for automatic detection.  ... 
doi:10.1016/j.future.2018.10.057 fatcat:pgnkhitq35e4vcijxfna2ttfu4

PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses

Qing Chen, Yuanzhe Chen, Dongyu Liu, Conglei Shi, Yingcai Wu, Huamin Qu
2016 IEEE Transactions on Visualization and Computer Graphics  
Such large amounts of multivariate data pose a new challenge in terms of analyzing online learning behaviors.  ...  Previous studies have mainly focused on the aggregate behaviors of learners from a summative view; however, few attempts have been made to conduct a detailed analysis of such behaviors.  ...  online learning behaviors based on video clickstream data from Coursera.  ... 
doi:10.1109/tvcg.2015.2505305 pmid:26661473 fatcat:jtzcgcjlabhfdlh7archkuewtm

"Your click decides your fate": Leveraging clickstream patterns from MOOC videos to infer students' information processing & attrition behavior [article]

Tanmay Sinha
2014 arXiv   pre-print
We leverage recurring click behaviors to differentiate distinct video watching profiles for students in MOOCs.  ...  As a research contribution, we operationalize video lecture clickstreams of students into behavioral actions, and construct a quantitative information processing index, that can aid instructors to better  ...  In the changed setup, we consider summarized behavioral category vectors (output from level 2) as column features.  ... 
arXiv:1407.7143v1 fatcat:mez3qrpcjvarhmfqdsnwuavmfy

Unsupervised Clickstream Clustering for User Behavior Analysis

Gang Wang, Xinyi Zhang, Shiliang Tang, Haitao Zheng, Ben Y. Zhao
2016 Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI '16  
In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner.  ...  For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agencies.  ... 
doi:10.1145/2858036.2858107 dblp:conf/chi/WangZTZZ16 fatcat:jxx7djf33fettcm2njlkappav4

Dropout Model Evaluation in MOOCs [article]

Josh Gardner, Christopher Brooks
2018 arXiv   pre-print
methods from raw data.  ...  -, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another.  ...  Future Research We hope that the methods presented in this paper are a catalyst for much-needed future exploration and adoption of methods for statistical evaluation of predictive models in MOOCs.  ... 
arXiv:1802.06009v1 fatcat:243jhs3ur5bntm2cxen4unchfu

MOOC performance prediction via clickstream data and social learning networks

Christopher G. Brinton, Mung Chiang
2015 2015 IEEE Conference on Computer Communications (INFOCOM)  
In doing so, we develop novel techniques that leverage behavioral data collected by MOOC platforms.  ...  Moreover, the improvement is particularly pronounced when considering the first few course weeks, demonstrating the "early detection" capability of such clickstream data.  ...  In this paper, using data from one of our own MOOC offerings, we applied some standard algorithms to CFA prediction in this setting, and showed how one type of behavioral data collected about students  ... 
doi:10.1109/infocom.2015.7218617 dblp:conf/infocom/BrintonC15 fatcat:hrhhjcghsncqxmosz74hm6bhaa

Web user profiles with time-decay and prototyping

Domen Košir, Igor Kononenko, Zoran Bosnić
2014 Applied intelligence (Boston)  
In our experimental work, we experimented extensively with two real data sets: data of an online advertising network and student data in an online e-learning environment.  ...  Non-invasive profiling methods monitor users' behavior and infer interest profiles from their past actions.  ...  The contextual profiles of web users are usually inferred from their clickstreams and related data.  ... 
doi:10.1007/s10489-014-0570-9 fatcat:aipcf4qkkjagnatjf74exflnue

Mining MOOC Clickstreams: On the Relationship Between Learner Behavior and Performance [article]

Christopher G. Brinton, Swapna Buccapatnam, Mung Chiang, H. V. Poor
2015 arXiv   pre-print
We study student behavior and performance in two Massive Open Online Courses (MOOCs).  ...  Since our prediction considers videos individually, these benefits also suggest that our models are useful in situations where there is limited training data, e.g., for early detection or in short courses  ...  We will also consider an algorithm that does not make use of clickstream data, to act as a baseline for evaluating the gain from incorporating behavior.  ... 
arXiv:1503.06489v2 fatcat:hdhajca6ubgiddrqntxvb6wabu

A Clickstream Data Analysis of the Differences between Visiting Behaviors of Desktop and Mobile Users

Tingting Jiang, Jiaqi Yang, Cong Yu, Yunxin Sang
2018 Data and Information Management  
This study, based on a log file from a cross-border E-commerce platform, conducted a clickstream data analysis to compare desktop and mobile users' visiting behavior.  ...  Mobile devices are gaining popularity among online shoppers whose behavior has been reshaped by the changes in screen size, interface, functionality, and context of use.  ...  Moreover, clickstream data has been applied in E-learning to improve the video lectures (Sinha, Jermann, Li, & Dillenbourg, 2014) , as well as to detect and predict students' activities and performance  ... 
doi:10.2478/dim-2018-0012 fatcat:mtfyad5wznehljhsnor6zweaue

Predicting Academic Performance of Students from VLE Big Data using Deep Learning Models

Hajra Waheed, Saeed-Ul Hassan, Naif Radi Aljohani, Julie Hardman, Raheel Nawaz
2019 Computers in Human Behavior  
This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures  ...  This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures  ...  Learning accuracy improves with the increased clickstream quarterly data, implying the possibility of early prediction of students' at-risk of failure, detecting early withdrawals and distinguishing students  ... 
doi:10.1016/j.chb.2019.106189 fatcat:ufaykjxcvngvzhmjghdm3ssxq4

Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data

Matt Crosslin, Kimberly Breuer, Nikola Milikić, Justin T. Dellinger
2021 Journal of Research in Innovative Teaching & Learning  
that is student-directed.Design/methodology/approachProcess mining analysis was utilized to examine and cluster clickstream data from an online college-level History course designed with dual modality  ...  Preliminary data for this paper was originally presented as a poster session at the Learning Analytics and Knowledge conference in 2019.  ...  choices can be determined from process mining analysis of available clickstream data?  ... 
doi:10.1108/jrit-03-2021-0024 fatcat:itmr35iutfgpxgw3msr5krd3hq

Tracking Changes in Students' Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining

Michelle Taub, Allison M. Banzon, Tom Zhang, Zhongzhou Chen
2022 Frontiers in Psychology  
This paper studies how students' SRL behavior and achievement goal orientation change over time in a large (N = 250) college introductory level physics course taught online.  ...  Students' SRL behavior was measured by analyzing their clickstream event traces interacting with online learning modules via a combination of trace clustering and process mining.  ...  In those cases, a different analysis scheme is needed to extract information about students' SRL behaviors from clickstream data that contain a much smaller set of event types and possible event orders  ... 
doi:10.3389/fpsyg.2022.813514 pmid:35369254 pmcid:PMC8968150 fatcat:2du6qkzj6zephg7sskf5lpxuoi

Analysis of the Factors Influencing Learners' Performance Prediction with Learning Analytics

Pedro Manuel Moreno-Marcos, Ting-Chuen Pong, Pedro J. Munoz-Merino, Carlos Delgado Kloos
2020 IEEE Access  
In this direction, this work aims to analyze how several factors can make an influence on the prediction of students' performance.  ...  These factors include the effect of previous grades, forum variables, variables related to exercises, clickstream data, course duration, type of assignments, data collection procedure, question format  ...  Moreover, it is important to differentiate between data about student information and progress, and clickstream data (i.e., data containing all the events student do in the platform, such as play, pause  ... 
doi:10.1109/access.2019.2963503 fatcat:lla7xz4v2jfohfck7ygnfsgnhu
« Previous Showing results 1 — 15 out of 463 results