short-paper

RABIT: Reflective Analytics for Business InTelligence

Authors:

Panayiotis Andreou,

Christos Amyrotos,

Andreas Pamboris,

Panagiotis GermanakosAuthors Info & Claims

CHI Greece 2021: CHI Greece 2021: 1st International Conference of the ACM Greek SIGCHI Chapter

Article No.: 13, Pages 1 - 8

https://doi.org/10.1145/3489410.3489423

Published: 25 November 2021 Publication History

Abstract

Self-tracking enables individuals to monitor things like their behavioral, biological, physical or environmental data, offering the means for reflection and self-growth. Reflective Analytics utilizes educational data mining and learning analytics to further empower the reflection process by unveiling hidden insights regarding one’s productivity. While a number of productivity monitoring systems exist, they focus solely on personal analytics and lack the ability to identify best practices within teams or organizations. We present RABIT (Reflective Analytics for Business InTelligence), a platform that enables the observation, analysis and reflection on analytical work patterns at various levels within an organization. It seamlessly monitors/logs fine-grained information regarding the users’ data exploration process and generates insights for the individual, team and organization. RABIT is evaluated in a real world setting with ten analysts and is shown to hold the promise for detecting problems and planning contingencies, identifying opportunities for change, and adopting best practices.

References

[1]

2021. Microsoft MyAnalytics. Retrieved April 07, 2021 from https://www.microsoft.com/en-ww/microsoft-365/business/myanalytics-personal-analytics

[2]

2021. Odoo Appraisals. Retrieved April 06, 2021 from https://www.odoo.com/app/appraisals

[3]

2021. OPEN HRMS. Retrieved April 06, 2021 from https://www.openhrms.com/

[4]

2021. Oracle Human Capital Management (HCM). Retrieved April 07, 2021 from https://www.oracle.com/uk/human-capital-management/

[5]

2021. OrangeHRM. Retrieved April 07, 2021 from https://www.orangehrm.com/

[6]

2021. ReviewSnap. Retrieved April 07, 2021 from https://www.reviewsnap.com/

[7]

2021. SAP SucccessFactors. Retrieved April 07, 2021 from https://www.sap.com/products/performance-goals/features.html

[8]

2021. Sentrifugo. Retrieved April 06, 2021 from http://www.sentrifugo.com/

[9]

2021. SnapEval. Retrieved April 07, 2021 from https://www.snapeval.com/

[10]

2021. Trakstar. Retrieved April 06, 2021 from https://www.trakstar.com/

[11]

2021. User Experience Questionnaire (UEQ). Retrieved April 06, 2021 from https://www.ueq-online.org/

[12]

Kenro Aihara and Atsuhiro Takasu. 2001. Category Based Customization Approach for Information Retrieval. In User Modeling 2001, Mathias Bauer, Piotr J Gmytrasiewicz, and Julita Vassileva (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 207–209.

Digital Library

[13]

Qasem A Al-Radaideh and Eman Al Nagi. 2012. Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance. International Journal of Advanced Computer Science and Applications 3, 2(2012). https://doi.org/10.14569/IJACSA.2012.030225

[14]

Liaqat Ali, Marek Hatala, Dragan Gašević, and Jelena Jovanović. 2012. A qualitative evaluation of evolution of a learning analytics tool. Computers and Education 58, 1 (jan 2012), 470–489. https://doi.org/10.1016/j.compedu.2011.08.030

Digital Library

[15]

Joseph E Beck and Beverly Park Woolf. 1998. Using a Learning Agent with a Student Model. In Intelligent Tutoring Systems, Barry P Goettl, Henry M Halff, Carol L Redfield, and Valerie J Shute (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 6–15.

Digital Library

[16]

Irad Ben-Gal, Shahar Weinstock, Gonen Singer, and Nicholas Bambos. 2019. Clustering Users by Their Mobility Behavioral Patterns. ACM Trans. Knowl. Discov. Data 13, 4, Article 45 (Aug. 2019), 28 pages. https://doi.org/10.1145/3322126

Digital Library

[17]

Daniel Billsus and Michael J Pazzani. 1999. A Hybrid User Model for News Story Classification. In UM99 User Modeling, Judy Kay (Ed.). Springer Vienna, Vienna, 99–108.

Digital Library

[18]

Paulo Blikstein and Marcelo Worsley. 2016. Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks. Journal of Learning Analytics 3, 2 (2016), 220–238. https://doi.org/10.18608/jla.2016.32.11

[19]

Zheng Chen, Fan Lin, Huan Liu, Yin Liu, Wei-Ying Ma, and Liu Wenyin. 2002. User Intention Modeling in Web Applications Using Data Mining. World Wide Web 5, 3 (2002), 181–191. https://doi.org/10.1023/A:1020980528899

Digital Library

[20]

Jordi Colomer, Maria Pallisera, Judit Fullana, Marc Pérez Burriel, and Rosario Fernández. 2013. Reflective Learning in Higher Education: A Comparative Analysis. Procedia - Social and Behavioral Sciences 93 (2013), 364–370. https://doi.org/10.1016/j.sbspro.2013.09.204

[21]

Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. 1996. The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM 39, 11 (Nov. 1996), 27–34. https://doi.org/10.1145/240455.240464

Digital Library

[22]

Zhabiz Gharibshah, Xingquan Zhu, Arthur Hainline, and Michael Conway. 2020. Deep Learning for User Interest and Response Prediction in Online Display Advertising. Data Science and Engineering 5, 1 (2020), 12–26. https://doi.org/10.1007/s41019-019-00115-y

[23]

Abir Gorrab, Ferihane Kboubi, Ali Jaffal, Bénédicte Le Grand, and Henda Ben Ghezala. 2017. Twitter User Profiling Model Based on Temporal Analysis of Hashtags and Social Interactions. In Natural Language Processing and Information Systems, Flavius Frasincar, Ashwin Ittoo, Le Minh Nguyen, and Elisabeth Métais (Eds.). Springer International Publishing, Cham, 124–130.

[24]

Shuchi Grover, Marie Bienkowski, Amir Tamrakar, Behjat Siddiquie, David Salter, and Ajay Divakaran. 2016. Multimodal Analytics to Study Collaborative Problem Solving in Pair Programming. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge(LAK ’16). Association for Computing Machinery, New York, NY, USA, 516–517. https://doi.org/10.1145/2883851.2883877

Digital Library

[25]

Tanja Joerding. 1999. A Temporary User Modeling Approach for Adaptive Shopping on the Web. In 2nd Workshop Adaptive Systems and User Modeling on the WWW, Toronto, Canada (1999).

[26]

Hyon Hee Kim, Jinnam Jo, and Donggeon Kim. 2012. Generation of Tag-Based User Profiles for Clustering Users in a Social Music Site. In Intelligent Information and Database Systems, Jeng-Shyang Pan, Shyi-Ming Chen, and Ngoc Thanh Nguyen(Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 51–61.

Digital Library

[27]

Sam Laird. 2012. Mashable - How Smartphones Are Changing Health Care. Retrieved March 20, 2013 from http://mashable.com/2012/09/26/smartphones-health-care-infographic

[28]

Timo Lampinen, Hannu Koivisto, and Tapani Honkanen. 2002. Profiling Network Applications with Fuzzy C-Means Clustering and Self-Organizing Map. In FSKD. Citeseer, Singapore, 300–304.

[29]

Leah P Macfadyen and Shane Dawson. 2010. Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education 54, 2 (2010), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008

Digital Library

[30]

Ben Medler. 2009. Generations of Game Analytics, Achievements and High Scores. Eludamos. Journal for Computer Game Culture 3 (2009), 177–194.

[31]

Bamshad Mobasher, Robert Cooley, and Jaideep Srivastava. 2000. Automatic Personalization Based on Web Usage Mining. Commun. ACM 43, 8 (aug 2000), 142–151. https://doi.org/10.1145/345124.345169

Digital Library

[32]

Alexandros Nanopoulos, Dimitris Katsaros, and Yannis Manolopoulos. 2001. Effective Prediction of Web-user Accesses: a Data Mining Approach. (2001).

[33]

Naren.J, Elakia, Gayathri, and Aarthi. 2014. Application of Data Mining in Educational Database for Predicting Behavioural Patterns of the Students. International Journal of Engineering and Technology vol 5(3) (06 2014), 4469–4472.

[34]

Jeffrey Norris. 2012. UCSF Self-Tracking May Become Key Element of Personalized Medicine. Retrieved March 20, 2013 from www.ucsf.edu/news/2012/10/12913/self-tracking-may-become-key-element-personalized-medicine

[35]

Pierre-Edouard Osche, Sylvain Castagnos, Amedeo Napoli, and Yannick Naudet. 2016. Walk the line: Toward an efficient user model for recommendations in museums. In 2016 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). 83–88. https://doi.org/10.1109/SMAP.2016.7753389

[36]

Zacharoula Papamitsiou and Anastasios A Economides. 2014. Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Journal of Educational Technology & Society 17, 4 (apr 2014), 49–64. http://www.jstor.org/stable/jeductechsoci.17.4.49

[37]

Jean-David Ruvini. 2003. Adapting to the User’s Internet Search Strategy. In User Modeling 2003, Peter Brusilovsky, Albert Corbett, and Fiorella de Rosis(Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 55–64.

Digital Library

[38]

Jose Luis Santos, Sten Govaerts, Katrien Verbert, and Erik Duval. 2012. Goal-Oriented Visualizations of Activity Tracking: A Case Study with Engineering Students. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge(LAK ’12). Association for Computing Machinery, New York, NY, USA, 143–152. https://doi.org/10.1145/2330601.2330639

Digital Library

[39]

Rituparna Sen and Mark H Hansen. 2003. Predicting Web Users’ Next Access Based on Log Data. Journal of Computational and Graphical Statistics 12, 1(2003), 143–155. https://doi.org/10.1198/1061860031275 arXiv:https://doi.org/10.1198/1061860031275

[40]

Michael Shepherd, Carolyn Watters, and Ajitha T Marath. 2002. Adaptive user modeling for filtering electronic news. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences. IEEE, Big Island, 1180–1188. https://doi.org/10.1109/HICSS.2002.994040

Digital Library

[41]

Laila Shoukry, Stefan Göbel, and Ralf Steinmetz. 2014. Learning Analytics and Serious Games: Trends and Considerations. In Proceedings of the 2014 ACM International Workshop on Serious Games(SeriousGames ’14). Association for Computing Machinery, New York, NY, USA, 21–26. https://doi.org/10.1145/2656719.2656729

Digital Library

[42]

George Siemens and Ryan S J d. Baker. 2012. Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge(LAK ’12). Association for Computing Machinery, New York, NY, USA, 252–254. https://doi.org/10.1145/2330601.2330661

Digital Library

[43]

Ihsan Topalli and Selçuk KILINÇ. 2020. User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28 (05 2020), 1567–1583. https://doi.org/10.3906/elk-1909-139

[44]

Victoria Tsiriga and Maria Virvou. 2002. Initializing the student model using stereotypes and machine learning. In IEEE International Conference on Systems, Man and Cybernetics, Vol. 2. IEEE, Yasmine Hammamet, 404–409. https://doi.org/10.1109/ICSMC.2002.1173446

[45]

Rüdiger Wirth and Jochen Hipp. 2000. CRISP-DM: Towards a standard process model for data mining. Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining (01 2000).

[46]

Marcelo Worsley, Dor Abrahamson, Paulo Blikstein, Shuchi Grover, Bertrand Schneider, and Mike Tissenbaum. 2016. Situating Multimodal Learning Analytics.

Cited By

El-khalili NAbu Arqoub MHasan MBanna AArafah M(2024)Empowering Learning Analytics with Business Intelligence2024 2nd International Conference on Cyber Resilience (ICCR)10.1109/ICCR61006.2024.10533111(1-6)Online publication date: 26-Feb-2024
https://doi.org/10.1109/ICCR61006.2024.10533111

Index Terms

RABIT: Reflective Analytics for Business InTelligence

Index terms have been assigned to the content through auto-classification.

Recommendations

Business Intelligence and Analytics: Research Directions

Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving ...
Big Data Analytics as a Service for Business Intelligence
Open and Big Data Management and Innovation
Abstract
This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture (BASOA), and applying BASOA to ...
Responsible Big Data Analytics for E-Business Services
ICBDR '21: Proceedings of the 5th International Conference on Big Data Research

This paper examines responsible big data analytics for e-business services and looks at how to use responsible big data analytics to obtain responsible e-business services. It addresses why responsibility matters to big data analytics and e-business ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

CHI Greece 2021: CHI Greece 2021: 1st International Conference of the ACM Greek SIGCHI Chapter

November 2021

172 pages

ISBN:9781450385787

DOI:10.1145/3489410

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 November 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

Research and Innovation Foundation Cyprus

Conference

CHI Greece 2021

CHI Greece 2021: 1st International Conference of the ACM Greek SIGCHI Chapter

November 25 - 27, 2021

Online (Athens, Greece), Greece

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
57
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

El-khalili NAbu Arqoub MHasan MBanna AArafah M(2024)Empowering Learning Analytics with Business Intelligence2024 2nd International Conference on Cyber Resilience (ICCR)10.1109/ICCR61006.2024.10533111(1-6)Online publication date: 26-Feb-2024
https://doi.org/10.1109/ICCR61006.2024.10533111

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents