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6. Improving Students' Performance with Time Management Skills

Author

Wilson, Robert, Joiner, Keith, and Abbasi, Alireza

Abstract

Research at the University of New South Wales with Defence Force Academy students found timemanagement skills significantly improve the academic results of male students but not female students when controlling for the usual effect of prior academic ability. While much previous research has revealed a positive link between time management and academic results within civilian environments, there is a lack of research on this link in dual-military or employment and academic settings. Our research also extends on many national and international studies' by researching the efficacy of a direct training intervention in time-management skills. The study is unique in evaluating the size of the likely effect on academic results. This study used a mixed-methods evaluation design and a standardised questionnaire to compare two groups of 70 first-year undergraduate students. The experimental group were taught time-management skills in a workshop setting, while a control group did not attend time-management workshops. While the time-management workshop interventions did not improve the academic performance of female students, female students did benefit comparably to males in fewer subject failings and improved attitudes towards time-management attributes. The findings confirm an effect between time-management skills and academic success that offers substantial savings from fewer lost students. Recommendations are made to improve time-management skills and academic performance for all transitioning university students and examine if this significant effect translates to other initial training employment.

Published
2021

22. A comprehensive health effects assessment of the use of sanitizers and disinfectants during COVID-19 pandemic: a global survey

Author

Hashemi, Fallah, Hoepner, Lori, Hamidinejad, Farahnaz Soleimani, Haluza, Daniela, Afrashteh, Sima, Abbasi, Alireza, Omeragić, Elma, Imamović, Belma, Rasheed, Narin A., Taher, Taqi M. J., Kurniasari, Fitri, Wazqar, Dhuha Youssef, Apalı, Özge Ceren, Yildirim, Ayca Demir, Zhao, Bo, Kalikyan, Zaruhi, Guo, Cui, Valbuena, Andrea Chong, Mititelu, Magdalena, Pando, Carolina Martínez, Saridi, Maria, Toska, Aikaterini, Cuba, Magalys Lopez, Kwadzokpui, Precious Kwablah, Tadele, Niguse, Nasibova, Tohfa, Harsch, Stefanie, Munkh-Erdene, Luvsan, Menawi, Wafaa, Evangelou, Efi, Dimova, Antoniya, Marinov, Dimitar, Dimitrova, Teodora, Shalimova, Anna, Fouly, Howieda, Suraya, Anna, da Silva Faquim, Juliana Pereira, Oumayma, Bouadil, Annunziato, Maria Antonieta, Lalo, Rezarta, Papastavrou, Evridiki, Ade, Anju D., Caminada, Susanna, Stojkov, Svetlana, Narvaez, Carmen Gloria, Mudau, Lutendo Sylvia, Rassas, Ines, Michel, Daphnee, Kaynar, Nur Sema, Iqbal, Sehar, Elshwekh, Halla, Hossain, Irin, AL-Fayyadh, Sadeq, Sydorchuk, Aniuta, Alnusairat, Dua’a M. H., Abdullahi, Asli Mohamed, Iqbal, Neelam, Pandey, Apsara, Gómez-Gómez, Brenda, Akyildiz, Aysenur Gunaydin, Morosan, Elena, Dwarica, Daniella, Dorj, Gantuya, Hasan, Sumaya Yusuf, Al-Shdayfat, Noha M., Knezevic, Bojana, Valladares, Wendy, Severi, Cecilia, Fuentes, Sofia Cuba, Augusto, Sofia, Sidorova, Elizaveta, Moelyaningrum, Anita Dewi, Alawad, Tafaul, Khalid, Atiqa, Elehamer, Nafisa M. K., Mihaylova, Anna, Tsigengagel, Oxana, Menouni, Aziza, Wojtecka, Agnieszka, Hod, Rozita, Idayat, Yusuf Banke, Othman, Khadija, Harfouch, Rim M., Paunov, Tsonco, Omar, Meruyert, Benderli, Nana Christine, Nurika, Globila, Amjad, Sana, Elnoamany, Salma, Elesrigy, Fatma, Shaban, Marwa Mamdouh, Acevedo-López, Doménica, Kartashova, Maria, Khalaf, Atika, Jaafar, Sabah Abdullah, Kadhim, Taisir A., Hweissa, Nada Ab, Teng, Yulong, Mohammed, Fatima E., Sasikumar, Thayahlini, Hikaambo, Christabel Nangandu, Kharat, Aditi, Lyamtseva, Ulyana, Aldeeb, Maya Arfan, Pawlas, Natalia, Khorolsuren, Lkhagvasuren, Koonjul, Roopeshwaree Pallavi, Maïnassara, Halima Boubacar, Chahal, Priyanka, Wangeci, Rose W., Kumar, Ainur B., Zamora-Corrales, Irina, Gracy, Stella, Mahamat, Maimouna, Adamczyk, Jakub, Rahman, Haliza Abdul, Matiashova, Lolita, Elsherif, Omneya Ezzat, Alkhateeb, Nazdar E., Aleaga, Yamilé, Bahrami, Shima, Al-salihy, Shaimaa Rahem, Cabrera-Galeana, Paula, Lalic-Popovic, Mladena, Brown-Myrie, Eugenie, Bhandari, Divya, Mayaboti, Cinderella Akbar, Stanišić, Svetlana, Pestic, Sanda Kreitmayer, Bektay, Muhammed Yunus, Al Sabbah, Haleama, Hashemi, Saber, Assia, Bouchetara, Merritt, Anne-Sophie, Ramzi, Zhian, Baboolal, Himawatee, Isstaif, Juman, Shami, Rula, Saad, Rahma, Nyirongo, Temwanani, and Hoseini, Mohammad

Published
2023
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35. TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrix

Author

Abbas, Khushnood, Abbasi, Alireza, Shi, Dong, Ling, Niu, Shang, Mingsheng, Liong, Chen, and Chen, Bolun

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

Understanding the evolutionary patterns of real-world evolving complex systems such as human interactions, transport networks, biological interactions, and computer networks has important implications in our daily lives. Predicting future links among the nodes in such networks reveals an important aspect of the evolution of temporal networks. To analyse networks, they are mapped to adjacency matrices, however, a single adjacency matrix cannot represent complex relationships (e.g. temporal pattern), and therefore, some approaches consider a simplified representation of temporal networks but in high-dimensional and generally sparse matrices. As a result, adjacency matrices cannot be directly used by machine learning models for making network or node level predictions. To overcome this problem, automated frameworks are proposed for learning low-dimensional vectors for nodes or edges, as state-of-the-art techniques in predicting temporal patterns in networks such as link prediction. However, these models fail to consider temporal dimensions of the networks. This gap motivated us to propose in this research a new node embedding technique which exploits the evolving nature of the networks considering a simple three-layer graph neural network at each time step, and extracting node orientation by Given's angle method. To prove our proposed algorithm's efficiency, we evaluated the efficiency of our proposed algorithm against six state-of-the-art benchmark network embedding models, on four real temporal networks data, and the results show our model outperforms other methods in predicting future links in temporal networks., Comment: IEEE double column 6 pages

Published
2020

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