To ensure productivity in the higher education sector, tracking lecture attendance of instructors and students is vital. Low attendance often leads to negative consequences in terms of optimal output for both individuals and the institution. Traditional methods of locating lecture rooms and tracking attendance are inefficient and error-prone. In this article, we propose Geo-Lecture, a location-based recommender algorithm which leverages a geographical information system (GIS). Using student and location data from a Technical University in Ghana, Geo-Lecture provides accurate, and real-time lecture attendance tracking. Evaluation results demonstrate that Geo-Lecture significantly outperforms contemporary methods with the highest precision of 0.31, recall of 0.34, and an F1-score of 0.32. Additionally, the mean absolute error (MAE) and Normalized MAE both illustrate results of 0.69 and 0.17, respectively, which are the lowest comparatively. These results validate Geo-Lecture's superiority in terms of accuracy, reliability, and usability in comparison to existing approaches.
AcharyaMYadavSMohbeyKK (2023) How can we create a recommender system for tourism? A location centric spatial binning-based methodology using social networks. International Journal of Information Management Data Insights3(1): 100161.
2.
AdomaviciusGBaumanKTuzhilinA, et al. (2021) Context-aware recommender systems: From foundations to recent developments. In: Recommender Systems Handbook. New York, NY: Springer US, 211–250.
3.
Al-NafjanAAlrashoudiNAlrasheedH (2022) Recommendation system algorithms on location-based social networks: Comparative study. Information13(4): 188.
4.
AnifowoseFKhoukhiAAbdulraheemA (2017) Investigating the effect of training–testing data stratification on the performance of soft computing techniques: An experimental study. Journal of Experimental and Theoretical Artificial Intelligence29(3): 517–535.
5.
ApoorvRMathurP (2016) Smart attendance management using bluetooth low energy and android. In: Proceedings of the IEEE Region 10 Conference (TENCON), pp.1048–1052.
6.
ArulampalamWNaylorRASmithJ (2012) Am I missing something? The effects of absence from class on student performance. Economics of Education Review31(4): 363–375.
7.
AsabereNYLawsonGBadu-MarfoG, et al. (2021) Classification of public health centres in Accra through a web-based portal integrated with geographical information system (GIS). Journal of Healthcare Engineering2021(1): 4178161.
8.
BaykanEHenzingerMMarianL, et al. (2011) A comprehensive study of features and algorithms for URL-based topic classification. ACM Transactions on the Web (TWEB)5(3): 1–29.
9.
BrunoGGiannikosI (2015) Location and GIS. In: LaporteGNickelSSaldanha da GamaF (eds) Location Science. Cham: Springer, 509–536.
10.
ClemensonGDMaselliAFiannacaAJ, et al. (2021) Rethinking GPS navigation: Creating cognitive maps through auditory clues. Scientific Reports11(1): 7764.
11.
DingZLiXJiangC, et al. (2018) Objectives and state-of-the-art of location-based social network recommender systems. ACM Computing Surveys51(1): 1–28.
12.
HameedSSaquibSMTul HassanM, et al. (2015) Radio frequency identification (RFID) based attendance and assessment system with wireless database records. Procedia-Social and Behavioral Sciences195: 2889–2895.
13.
HuangHGartnerGKrispJM, et al. (2018) Location based services: Ongoing evolution and research agenda. Journal of Location Based Services12(2): 63–93.
14.
JinCNaraAYangJA, et al. (2020) Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM). Transactions in GIS24(1): 104–122.
15.
KhanSLiuXShakilKA, et al. (2017) A survey on scholarly data: From big data perspective. Information Processing and Management53(4): 923–944.
16.
KhongRWDunnJSLimCM, et al. (2016) Why do students attend lectures? Exploring justifications for attendance among undergraduate students from a British University in Asia. The Journal of Developing Areas50(5): 497–506.
17.
KumarSQadeerMAGuptaA (2009) Location based services using android (LBSOID). In: Proceedings of the IEEE International Conference on Internet Multimedia Services Architecture and Applications (IMSAA), pp.1–5.
18.
LalaOGAworindeHOOgunbanwoAD, et al. (2022) A mobile web application for attendance management using geolocation. Adeleke University Journal of Science (AUJS)1(2): 459–467.
19.
LeQTAntoshchukSNguyenTKT, et al. (2020) Automated student attendance monitoring system in classroom based on convolutional neural networks. Applied Aspects of Information Technology3(3): 179–190.
20.
LianDWuYGeY, et al. (2020) Geography-aware sequential location recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2009–2019.
21.
LodhaRGuptaSJainH, et al. (2015) Bluetooth smart based attendance management system. Procedia Computer Science45: 524–527.
22.
McMahonDCihakDFWrightR (2015) Augmented reality as a navigation tool to employment opportunities for postsecondary education students with intellectual disabilities and autism. Journal of Research on Technology in Education47(3): 157–172.
23.
MohandesMA (2017) Class attendance management system using NFC mobile devices. Intelligent Automation and Soft Computing23(2): 251–259.
24.
MooresEBirdiGKHigsonHE (2019) Determinants of university students’ attendance. Educational Research61(4): 371–387.
25.
OlmoFHGaudiosoE (2008) Evaluation of recommender systems: A new approach. Expert Systems with Applications35(3): 790–804.
26.
RaviLSubramaniyaswamyVVijayakumarV, et al. (2019) Hybrid location-based recommender system for mobility and travel planning. Mobile Networks and Applications24: 1226–1239.
27.
RiehlSK (2020). Property recommendation system with geospatial data analytics and natural language processing for urban land use. Master Thesis, Cleveland State University.
28.
RisimatiBGumboT (2018) Exploring the applicability of location-based services to delineate the state public transport routes integratedness within the city of Johannesburg. Infrastructures3(3): 28–41.
29.
SánchezPBellogínA (2022) Point-of-interest recommender systems based on location-based social networks: A survey from an experimental perspective. ACM Computing Surveys54(11s): 1–37.
30.
SaraswatGGargV (2016) Beacon controlled campus surveillance. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.2582–2586.
31.
SeetharamanPMathewSKDe’R (2024) Does location matter in IS research? A developing country perspective from India. Information Systems Journal: 1–32. DOI:10.1111/isj.12521.
32.
SilveiraTZhangMLinX, et al. (2019) How good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics10: 813–831.
33.
SokoH (2024) Attendance tracking system using geofencing: A web-based automated solution for higher education. i-Manager’s Journal on Information Technology13(1): 1–9.
34.
StojanovicDHDjordjevic-KajanSJ (2001) Developing location-based services from a GIS perspective. In: Proceedings of the 5th IEEE International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service. TELSIKS 2001. Proceedings of Papers (Cat. No. 01EX517), pp. 459–462.
35.
SultanaSEnayetAMouriIJ (2015) A smart, location based time and attendance tracking system using android application. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)5(1): 1–5.
36.
TammYMDamdinovRVasilevA (2021) Quality metrics in recommender systems: Do we calculate metrics consistently? In: Proceedings of the 15th ACM Conference on Recommender Systems, pp. 708–713.
37.
VasudevanSKVenkatachalamKShreeH, et al. (2020) An intelligent and interactive AR-based location identifier for indoor navigation. International Journal of Advanced Intelligence Paradigms15(1): 32–50.
38.
WestermanJWPerez-BatresLACoffeyBS, et al. (2011) The relationship between undergraduate attendance and performance revisited: Alignment of student and instructor goals. Decision Sciences Journal of Innovative Education9(1): 49–67.
39.
XiaFWangWBekeleTM, et al. (2017) Big scholarly data: A survey. IEEE Transactions on Big Data3(1): 18–35.
40.
Yue-QiangRZeWXiao-NaS, et al. (2019) A multi-element hybrid location recommendation algorithm for location based social networks. IEEE Access7: 100416–100427.
41.
ZhaoYZhangTDasguptaRK, et al. (2023) Narrowing the age-based digital divide: Developing digital capability through social activities. Information Systems Journal33(2): 268–298.
42.
ZhouXTianJPengJet al. (2021). A smart tourism recommendation algorithm based on cellular geospatial clustering and multivariate weighted collaborative filtering. ISPRS International Journal of Geo-Information, 10(9), 1–27.
