SAAS: Creation of an e-Advising Tool to Augment Traditional Advising Methods

Shana, Zuhrieh; Abdullah, Shubair A.

doi:10.5539/cis.v7n1p41

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Garger et al (2010), for example, investigated the new perceptions of future student performance which is based on reviewed literature with a non-experimental design of a model of student's social integration and GPA prediction. Shana and Abdullah (2014) described the design and creation of electronic Academic Advisor System termed as SAAS to support conventional procedures of a human advisor. Kouatli (2015) used hybrid intelligent technique composed of fuzzy logic and, genetic Algorithm to prototype a decision support system to predict the future GPA level for students during the admission process depending on the student performance of high school, sophomore exam and an English exam.…”

Section: Academic Advisor System: a Student Questionnairementioning

confidence: 99%

The need for social and academic responsibility advisor (SARA): a catalyst toward the sustainability of educational institutes

Kouatli

2019

SRJ

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Purpose The purpose of this paper is to investigate elements of socio-academic-related sustainability in educational institutes and propose a structure of an advising system that can aid the most critical stakeholders in such educational institutes, i.e the student. Hence, after reviewing the contemporary University Social Responsibility (USR) Venn diagram, the paper focuses on the need to develop a social and academic responsibility advisor (SARA) system as a catalyst toward fulfilling social responsibility to the most important stakeholder and alternatively leads to enhanced sustainability of such educational institutes. Design/methodology/approach A combination of research methods used in this paper, defined as by identifying the need for SARA from a literature survey. By distributing a questionnaire to students investigating their desire of an academic advisor system and by establishing a focus group to study the academic and social aspects and its implications to students’ “quality of life” as an essential aspect toward the educational institutes' sustainability. Various issues related to the features of the SARA discussed. Findings Literature review shows only a few articles combine both aspects of advising activities (social and academic), most of which are not interrelated to the sustainability of educational institutes. This paper highlights the need and connectivity of SARA to contemporary USR sustainability. The descriptive statistics of the questionnaire showed about 86 per cent of student participants interested in applying the proposed features. The outcome of the focus group resulted in more detailed features of academic and social aspects of the expected SARA system. Research limitations/implications The proposed features of SARA described where the inter-related social and academic activities could be managed, logged and used by students. The proposed “heterogeneous study group” was investigated by observation implemented in four different courses in two different semesters. The result was not conclusive, and further study recommended. Even though this experimentation was not conclusive, the lesson learned from this study highlighted different issues associated with “study groups” within a course. Practical implications Applicability of SARA would enhance the quality of life of students in general and provide a mechanism to motivate low aptitude students to get engaged with study and projects with high-aptitude students. Social implications Avoiding the “feel” of isolation by some students due to the inability to find study partner who can also act as mentor to a study group. The proposed “Heterogeneous study group” would provide a mechanism for “Practiced student-to-student Social responsibility”. Originality/value The paper highlights and proves the need for SARA in contemporary USR where SARA can act as a catalyst for enhancing the socio-academic zone of the reviewed USR Venn diagram. Features of SARA identified as an outcome of the study in this paper. Proposal of “Heterogeneous Study Group” was proposed as mechanism of “social learning”. “Group health” testing was proposed as a criteria resulted from a balance between collaboration, social affinity and project effort.

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Section: Academic Advisor System: a Student Questionnairementioning

confidence: 99%

The need for social and academic responsibility advisor (SARA): a catalyst toward the sustainability of educational institutes

Kouatli

2019

SRJ

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“…Several studies have investigated the impact of academic e-Advising on students. Zuhrieh and Shubair (2014) revealed that students appear to favor a blended advising approach. In such an approach, the advising process utilizes an e-Advising system and traditional face-to-face advising.…”

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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“…Other custom solutions have been developed such as a custom "e-advising" tool. Such a tool includes the means to create custom study plans as well as a schedule builder [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Do degree maps facilitate student success?

Marion¹,

Ruckert²,

Richardson³

2014

EPI

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The purpose of this mixed method study was to explore the potential impact of degree maps on facilitating student success. The concept grew out of quantitative evidence suggesting students using degree maps are more likely to enroll in our programs, continue in our programs, register for more classes per term, and have better graduation rates than those students who do not use a degree map. Our methodology included qualitative coding of focus group responses (n = 28) then using those emerging themes to inform a survey instrument to collect student perceptions about the degree map's impact (n = 211). This study's findings suggest the degree map is a powerful communication tool for advisors to help students through class selection and sequencing. The results provide insight into the differences between undergraduate and graduate student's needs.

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SAAS: Creation of an e-Advising Tool to Augment Traditional Advising Methods

Cited by 4 publications

References 7 publications

The need for social and academic responsibility advisor (SARA): a catalyst toward the sustainability of educational institutes

The need for social and academic responsibility advisor (SARA): a catalyst toward the sustainability of educational institutes

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

Do degree maps facilitate student success?

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