scholarly journals The Influence of Architecture Students’ Learning Approaches on their Academic Performance in Two Nigeria Universities

2021 ◽  
Vol 20 (2) ◽  
pp. 137-151
Author(s):  
Gabriel Sen ◽  
Albert Adeboye ◽  
Oluwole Alagbe
Keyword(s):  
Regression Analysis ◽  
Academic Performance ◽  
Deep Learning ◽  
Pilot Study ◽  
Statistical Analyses ◽  
Future Research ◽  
Learning Approaches ◽  
Research Needs ◽  
Heterogeneous Samples ◽  
And Gender

The paper was a pilot study that examined learning approaches of architecture students; variability of approaches by university type and gender and; influence of architecture students’ learning approaches on their academic performance. The sample was 349 architecture students from two universities. Descriptive and statistical analyses were used. Results revealed predominant use of deep learning approaches by students. Furthermore, learning approaches neither significantly differed by university type nor gender. Regression analysis revealed that demographic factors accounted for 2.9% of variation in academic performance (F (2,346) = 6.2, p = 0.002, R2 = 0.029, f2 = 0.029) and when learning approaches were also entered the model accounted for 4.4% of variation in academic performance (F (14,334) =2.2, p =0.009, R2 = 0.044, f2=0.044). Deep learning approaches significantly and positively influenced variation in academic performance while surface learning approaches significantly and negatively influenced academic performance. This implies that architectural educators should use instructional methods that encourage deep approaches. Future research needs to use larger and more heterogeneous samples for confirmation of results.

scholarly journals MOTIVATION, LEARNING STRATEGIES AND PERFORMANCE AMONG BUSINESS UNDERGRADUATES AT UNIVERSITY COLLEGES IN SWEDEN

2019 ◽  
Vol 17 (2) ◽  
pp. 111-133
Author(s):  
Elias Bengtsson ◽  
Britta Teleman
Keyword(s):  
Academic Performance ◽  
Deep Learning ◽  
Learning Strategies ◽  
Business Students ◽  
Student Characteristics ◽  
Learning Approaches ◽  
University College ◽  
And Performance ◽  
And Gender ◽  
Learning And Performance

Purpose – This paper brings new material to the understanding of interlinkages between motivation, learning and performance in academic contexts. By investigating these interlinkages in a new context – students of business and management at a Swedish university college – it seeks to answer the following research questions: How do students’ degree and type of motivation relate to their learning strategies?; how do students’ degree and type of motivation and learning strategies relate to their academic success?; and how do student characteristics in terms of experience and gender influence the nature and strength of these relationships? Research methodology – The data used in this paper is based on student surveys and a centralised system of reporting and archiving academic results. The latter contains information on the academic performance of individual students, whereas the surveys gathered information on the students’ background characteristics (experience and gender), their motivation for pursuing academic studies and their learning strategies. The difference in proportion tests and OLS regressions were then applied to investigate differences between student groups and relationships between the different variables. Findings – The findings reveal that business students are more extrinsically than intrinsically motivated; that deep learning approaches lead to higher grades for particular examination forms, and that female students are typically more intrinsically motivated, engage more in deep learning approaches and perform better than their male counterparts. Practical implications – The findings suggest that practitioners in higher education involved with the business and/or university college students have good reasons to stimulate motivation generally, and intrinsic motivation in particular. However, this must be accompanied by examination forms that promote deep learning. Originality/Value – In contrast to most research, this paper focuses on the interlinkages between motivation, learning and performance among business students in a university college setting. This contrasts most research on this topic which tends to be focused on university students, particularly in the US, in other fields of study or accounting. Moreover, this paper also takes student characteristics into account and uses a variety of measures to operationalise academic performance.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals MUHASEBE EĞİTİMİ ALAN ÖĞRENCİLERİN DERİN VE YÜZEYSEL ÖĞRENME YAKLAŞIMLARININ ÇEŞİTLİ DEĞİŞKENLER AÇISINDAN İNCELENMESİ

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Meryem ÖZTÜRK
Keyword(s):  
High School ◽  
Deep Learning ◽  
Significant Relationship ◽  
School Type ◽  
Preference Order ◽  
Learning Approaches ◽  
First Year Students ◽  
Accounting Courses ◽  
Surface Learning ◽  
And Gender

The purpose of the study is to determine whether there is a relationship between the learning approaches of the accounting students and demographic variables, the attendance to accounting courses and the daily repetition of accounting courses. In the scope of the study, a questionnaire was applied to Atatürk University Erzurum Vocational School Accountancy and Tax Applications Program students. According to the results of study, it was determined that there was a statistically significant relationship between the students' deep learning approach and daily repetition of accounting subject, the high school type they graduate and classroom level; it was determined that there was a statistically significant relationship between the students' surface learning tendency and gender, university preference order, attendance to accounting courses and daily repetition of accounting courses. When compared to the first-year students second-year students have higher deep learning tendencies and when compared to the other students, the students who graduated from the Open Education High School have higher than deep learning tendencies. The surface learning tendencies of the students who prefer the program in the first order is higher than those who prefer the program in the next order and the surface learning tendencies of man students is higher than female students. In addition, while deep learning tendency of students who daily repetition of accounting courses have a higher degree than other students, surface learning tendencies are lower.

scholarly journals Relationship between Students’ Involvement in Decision-Making and Academic Performance in Public Secondary Schools of Kenya

2021 ◽  
Vol 1 (2) ◽  
pp. p1
Author(s):  
Justus B. Maende
Keyword(s):  
Decision Making ◽  
Regression Analysis ◽  
Academic Performance ◽  
Secondary School Principals ◽  
Simple Random Sampling ◽  
Future Research ◽  
Validity And Reliability ◽  
Policy Makers ◽  
Baseline Information ◽  
Level Of Significance

Secondary school principals play a key role in decision-making leading to students’ academic performance. There was a decline in the percentage of the examination candidates from Kakamega County who were selected to join public universities from the year 2011 to 2015. This study intended to establish the relationship between students’ involvement in decision-making by principals and academic performance. Respondents were sampled by simple random sampling. Pre-testing of instruments of data collection was undertaken to ensure validity and reliability of the instruments. Data was collected from 36 principals, 199 teachers and 393 Form 4 students by use of questionnaire and interview schedule. Research experts determined validity of the instruments. Data was analyzed using descriptive statistics, frequencies, percentages, means, cross tabulation and Pearson’s correlation. Hypotheses were tested through regression analysis at 0.05 level of significance. Regression analysis revealed that students’ involvement in decision-making explained 24.6%, and of the variation in academic performance. Leadership functions such as students allowed to elect prefects, prefects attending staff/BoM meetings. It was recommended that principals should involve students in decision making. This study would be significant to policy makers, principals, teachers and other education stakeholders in Kenya. The study would also form baseline information for future research.

Inquiry and Gender Inequity in the Undergraduate Mathematics Classroom

2020 ◽  
Vol 51 (4) ◽  
pp. 504-516 ◽  
Author(s):  
Estrella Johnson ◽  
Christine Andrews-Larson ◽  
Karen Keene ◽  
Kathleen Melhuish ◽  
Rachel Keller ◽  
...  
Keyword(s):  
Hierarchical Linear Modeling ◽  
Mathematics Classroom ◽  
Gender Performance ◽  
Future Research ◽  
Learning Approaches ◽  
Linear Modeling ◽  
Gender Inequity ◽  
Student Groups ◽  
Content Assessment ◽  
And Gender

Our field has generally reached a consensus that active-learning approaches improve student success; however, there is a need to explore the ways that particular instructional approaches affect various student groups. We examined the relationship between gender and student learning outcomes in one context: inquiry-oriented abstract algebra. Using hierarchical linear modeling, we analyzed content assessment data from 522 students. We detected a gender performance difference (with men outperforming women) in the inquiry-oriented classes that was not present in other classes. We take the differential result between men and women to be evidence of gender inequity in our context. In response to these findings, we present avenues for future research on the gendered experiences of students in such classes.

scholarly journals Alcohol and Substance Use in the Jewish Community: A Pilot Study

2015 ◽  
Vol 2015 ◽  
pp. 1-4 ◽  
Author(s):  
Melanie Baruch ◽  
Abraham Benarroch ◽  
Gary E. Rockman
Keyword(s):  
Substance Use ◽  
Pilot Study ◽  
Family History ◽  
Jewish Community ◽  
Preliminary Evidence ◽  
Future Research ◽  
Research Needs ◽  
Community Knowledge ◽  
Jewish Communities ◽  
History Of

Awareness of addictions in the Jewish community is becoming increasingly prevalent, and yet, a gap exists in the literature regarding addictions in this community. Knowledge about the prevalence of addictions within Jewish communities is limited; some believe that Jews cannot be affected by addictions. To address this gap, a pilot study was conducted to gather preliminary evidence relating to addictions and substance use in the Jewish community. Results indicate that a significant portion of the Jewish community knows someone affected by an addiction and that over 20% have a family history of addiction. Future research needs are discussed.

scholarly journals On kitsch and kic: Comparing kitsch concepts from Bavaria, Serbia and Slovenia

Psihologija ◽  
2017 ◽  
Vol 50 (3) ◽  
pp. 357-381 ◽  
Author(s):  
Stefan Ortlieb ◽  
Ivan Stojilovic ◽  
Danaja Rutar ◽  
Uwe Fischer ◽  
Claus-Christian Carbon
Keyword(s):  
Regression Analysis ◽  
Pilot Study ◽  
Value Orientations ◽  
Future Research ◽  
Perceived Threat ◽  
Ambiguity Tolerance ◽  
Multilevel Regression ◽  
German Word ◽  
Crossed Random Effects ◽  
Self Transcendence

The German word kitsch has been internationally successful. Today, it is commonly used in many modern languages including Serbian and Slovenian (kic)-but does it mean the same? In a pilot study, thirty-six volunteers from Bavaria, Serbia and Slovenia rated two hundred images of kitsch objects in terms of liking, familiarity, determinacy, arousal, perceived threat, and kitschiness. Additionally, art expertise, ambiguity tolerance, and value orientations were assessed. Multilevel regression analysis with crossed random effects was used to explore crosscultural differences: Regardless of cultural background, liking of kitsch objects was positively linked to emotionally arousing items with non-threatening content. Self-transcendence was positively linked to liking, while ambiguity of the parental image was concordantly associated with kitschiness. For participants from Serbia and Slovenia, threatening content was correlated with kitschiness, while participants from Bavaria rated determinate items as kitschier. Results are discussed with regard to literature on kitsch and implications for future research.

scholarly journals Road Extraction from Remotely Sensed Data: A Review

2021 ◽  
Author(s):  
Mohd Jawed Khan ◽  
Pankaj Pratap Singh
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Remotely Sensed ◽  
Future Research ◽  
Learning Approaches ◽  
Road Extraction ◽  
Remotely Sensed Data ◽  
Remotely Sensed Imagery ◽  
Urban Rural ◽  
Future Research Directions

Up-to-date road networks are crucial and challenging in computer vision tasks. Road extraction is yet important for vehicle navigation, urban-rural planning, disaster relief, traffic management, road monitoring and others. Road network maps facilitate a great number of applications in our everyday life. Therefore, a systematic review of deep learning approaches applied to remotely sensed imagery for road extraction is conducted in this paper. Four main types of deep learning approaches, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models are presented in this paper. We also compare these various deep learning models applied to remotely sensed imagery to show their performances in extracting road parts from high-resolution remote sensed imagery. Later future research directions and research gaps are described.

scholarly journals A Review on the Detection of Offensive Content in Social Media Platforms

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Solomon Akinboro ◽  
Oluwadamilola Adebusoye ◽  
Akintoye Onamade
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hybrid Methods ◽  
Future Research ◽  
Learning Approaches ◽  
Hybrid Approaches ◽  
Use Of Social Media

Offensive content refers to messages which are socially unacceptable including vulgar or derogatory messages. As the use of social media increases worldwide, social media administrators are faced with the challenges of tackling the inclusion of offensive content, to ensure clean and non-abusive or offensive conversations on the platforms they provide.  This work organizes and describes techniques used for the automated detection of offensive languages in social media content in recent times, providing a structured overview of previous approaches, including algorithms, methods and main features used.   Selection was from peer-reviewed articles on Google scholar. Search terms include: Profane words, natural language processing, multilingual context, hybrid methods for detecting profane words and deep learning approach for detecting profane words. Exclusions were made based on some criteria. Initial search returned 203 of which only 40 studies met the inclusion criteria; 6 were on natural language processing, 6 studies were on Deep learning approaches, 5 reports analysed hybrid approaches, multi-level classification/multi-lingual classification appear in 13 reports while 10 reports were on other related methods.The limitations of previous efforts to tackle the challenges with regards to the detection of offensive contents are highlighted to aid future research in this area.  Keywords— algorithm, offensive content, profane words, social media, texts

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajshree Varma ◽  
Yugandhara Verma ◽  
Priya Vijayvargiya ◽  
Prathamesh P. Churi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Low Cost ◽  
Online Communication ◽  
Future Research ◽  
Learning Approaches ◽  
Fake News ◽  
Content Type ◽  
Human Ability ◽  
Detection Technologies

PurposeThe rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.Design/methodology/approachThe detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.FindingsThe paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.Originality/valueThe study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

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