Academic Advising Problems at Al-Quds University as Perceived by Students, and Its Relationship with Student’s Academic Performance

Banat, Bassam Yousef Ibrahim

doi:10.5901/mjss.2015.v4n1p97

Cited by 10 publications

(16 citation statements)

References 14 publications

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“…This finding was consistent with the result of Hsu and Bailey (2011). In contrast, Young-Jones et al (2013) and Banat (2015) found that gender demonstrated a difference, statistically, in academic advising service students' satisfaction. Female students indicate a higher mean value of 4.053 compared to male students, even if the number of male students is higher than female students.…”

Section: Significant Difference In Students' Satisfaction Across the Various Demographic Variablesmentioning

confidence: 81%

Students' Satisfaction towards Academic Advising Service

Ismail¹,

Roslan²,

Ismail³

et al. 2021

AJUE

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Effective academic advising service has been found to bring positive benefits to educational institutions to increase students' retention rates and graduation. This study sought to find the essential attributes in public university academic advising service in UiTM Pasir Gudang campus, Malaysia, which may be used to improve the service provided to the students. Quantitative research was conducted using a questionnaire adapted from Parasuraman's SERVQUAL model with a sample size of 221. The study aimed to examine the relationship between service quality dimensions and students' satisfaction. Descriptive statistics, correlation, and multiple regression analysis were used to analyse the questionnaires. Multiple regression analyses results showed reliability and empathy were two dimensions that had significant relationship with students' satisfaction. The results also demonstrated a statistical difference in academic advising service satisfaction among the students according to their years of study and meeting frequency with advisors. The findings of this study may be valuable for the institution to understand the factors that are most significant to further improve the students' satisfaction level in academic advising service. Keywords: Academic advising, Students’ satisfaction, Higher education, Service quality

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Section: Significant Difference In Students' Satisfaction Across the Various Demographic Variablesmentioning

confidence: 81%

Students' Satisfaction towards Academic Advising Service

Ismail¹,

Roslan²,

Ismail³

et al. 2021

AJUE

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show abstract

“…According to Nguyen et al (2008), academic advising models vary considerably, and little thought is given to the efficiency and adequacy of these models. They introduced a robust academic advising framework that focused on combining technology-enhanced learning theories into a pedagogy-driven and service-oriented architecture (Banat, 2015).…”

Section: Literature Review Academic Advicementioning

confidence: 99%

“…An interesting study by Banat (2015) highlights advising-related issues at Al-Quds University, as seen by students, and its consequences on students' academic success. Three hundred and sixty-nine fulltime college students at Al-Quds University were surveyed.…”

Section: Literature Review Academic Advicementioning

confidence: 99%

A Cognitive Knowledge-based Model for an Academic Adaptive e-Advising System

Al-Hunaiyyan

Bimba

Al-Sharhan

2020

IJIKM

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Aim/Purpose: This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background: A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology: This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution: The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are too rudimentary to take student characteristics (e.g., knowledge level, learning style, performance, demographics) into consideration. For the online systems that have replaced face-to-face academic advising to be effective, they need to take into consideration the dynamic nature of contemporary students and academic settings. Findings: The proposed algorithms can accommodate a highly diverse student body by providing information tailored to each student. The academic and student elements are represented as an Object-Attribute-Relationship (OAR) model. Recommendations for Practitioners: The model proposed here provides insight into the potential relationships between students’ characteristics and their academic standing. Furthermore, this novel e-Advising system provides large quantities of data and a platform through which to query students, which should enable developing more effective, knowledge-based approaches to academic advising. Recommendation for Researchers: The proposed model provides researches with a framework to incorporate various academic and student characteristics to determine the optimal advisory factors that affect a student’s performance. Impact on Society: The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advice to students. The proposed approach can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to learning. Future Research: In future studies, the proposed algorithms will be implemented, and the adaptive e-Advising model will be tested on real-world data and then further improved to cater to specific academic settings. The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advisory to students. The approach proposed can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to course recommendation.

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“…(15) (16) The questionnaire used was in the Arabic language, and its validity and reliability was confirmed through a previous study held in Al-Quds University and using exploratory factor analysis; the factor loading for all items exceeded 0.55 (0.59 to 0.78), and the Cronbach's Alpha and Guttman split-half coefficients were 0.81 and 0.80, respectively. (17) The EI score for each individual was calculated by adding the score obtained from each answer in the questionnaire and dividing by 33, giving a summary value with minimum of 1 and maximum of 5. Based on this score, the respondents were ranked into three categories (low EI, moderate EI, high EI), shown in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…Based on this score, the respondents were ranked into three categories (low EI, moderate EI, high EI), shown in Table 2. (17) Students were additionally categorized into levels defined according to their academic year:…”

Section: Methodsmentioning

confidence: 99%

Emotional Intelligence Among Medical Students and Residents in Palestine: A Cross-sectional Study

et al.

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Objective: In medical education and clinical practice, emotional intelligence (EI) has been related to improved doctor-patient relationships. Here, EI was measured among Palestinian medical stu-dents in two stages of their studies, clinical and basic sciences, and factors that may affect it were assessed. Moreover, EI scores were compared between participating universities to detect possible differences. Methods: A quantitative, cross-sectional, questionnaire-based, online survey was conducted on 692 medical students attending Al-Quds and Al-Najah Universities in Palestine. Emotional intel-ligence was evaluated using the 33-item scale introduced by Schutte et al. (1998). Data was ana-lyzed in a quantitative manner using the Statistical Package for Social Sciences (Ver. 20.0). Results: The mean EI was 3.83 (SD=0.41) out of maximum possible score of 5, with 69.1% hav-ing high EI (>3.68). EI decreased significantly at α[?]0.05 across basic and clinical stages of study, with a negative correlation between EI and academic year (PCC= -0.086). This indicates that as academic year increases, EI decreases (p=0.023). Moreover, EI is affected positively and signifi-cantly at α[?]0.05 by having a hobby or doing extracurricular activities.In addition, students who indicate continual regret for studying medicine tend to have lower EI, which may reflect a lack of interest in studying this field. Conclusion: The study found no significant differences in mean EI at α [?]0.05 between the students of Al-Quds (mean=3.83) and Al-Najah universities (mean=3.84). At these universities, par-ticipating medical students, both male and female, had relatively high levels of emotional intelli-gence. Students in the clinical stage had lower EI than those in the basic sciences stage, which indicates a conflict between objectivity and humanity while training clinically. Therefore, emo-tional support during clinical years would aid in improving EI. Moreover, EI being affected by hobbies or extracurricular activities indicates that EI can be modulated through encouragement of such activities. WHAT'S KNOWN? (what is already known about this subject?)EI is a very important parameter in doctor's life, it is associated with improved physical and psychological health, leads to better stress coping, lowers burnout, creates effective teamwork between medical staff. Higher EI also enhances leadership and improves doctor-patient relationship. Specifically, EI among medical students was previously studies and was shown to be affected by their stage of study. WHAT'S NEW? (what does this study contribute to the literature?)This study shows that EI among medical students in the sample tested declines as medical students progress from basic to clinical stage; the study find relationship between EI and hobbies, extracurricular activities and satisfaction in studying medicine. The study indicates that EI can be modulated through encouragement of such activities.

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Academic Advising Problems at Al-Quds University as Perceived by Students, and Its Relationship with Student’s Academic Performance

Abstract: This current study explores the main academic advising problems at Al-Quds

Cited by 10 publications

References 14 publications

Students' Satisfaction towards Academic Advising Service

Students' Satisfaction towards Academic Advising Service

A Cognitive Knowledge-based Model for an Academic Adaptive e-Advising System

Emotional Intelligence Among Medical Students and Residents in Palestine: A Cross-sectional Study

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