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The study explores factors affecting university students' behavioural intentions in adopting an academic advising chatbot. The study focuses on functional, socio-emotional, and relational factors affecting students' acceptance of an AI-driven academic advising chatbot. The research is based on a conceptual model derived from several constructs of traditional technology acceptance models, TAM, UTAUT, the latest AI-driven self-service technologies models, the Service Robot Acceptance (sRAM) model, and the intrinsic motivation Self Determination Theory (SDT) model. The proposed conceptual model has been tailored to an educational context. A questionnaire Survey of Non-purposive sampling technique was applied to collect data points from 207 university students from two major universities in the UAE. Subsequently, PLS-SEM causal modelling was applied for hypothesis testing. The results revealed that the functional elements, perceived ease of use and social influence significantly affect behavioural intention for chatbots' acceptance. However, perceived usefulness, autonomy, and trust did not show significant evidence of influence on the acceptance of an advising chatbot. The study reviews chatbot literature and presents recommendations for educational institutions to implement AI-driven chatbots effectively for academic advising. It is one of the first studies that assesses and examines factors that impact the willingness of higher education students to accept AI-driven academic advising chatbots. This study presents several theoretical contributions and practical implications for successful deployment of service-oriented chatbots for academic advising in the educational sector. [ABSTRACT FROM AUTHOR]

The purpose of this study was to share the design and development case of Advising Virtual Assistant (AVA), a chatbot created to provide support in academic advising in higher education. By analyzing participants' usage data and chatbot performance, we attempted to understand how students engage with AVA to fulfill their advising needs. AVA effectively delegated repetitive and prescriptive advising services thus enabling efficient utilization of human resources. Students' usage data and chatbot performance data showed increased engagement with academic advising and faster, readily available access to academic advising resources. Additionally, this study presented insights gained from the challenges faced during the integration of the chatbot and suggested directions for future research. The findings of this study are expected to aid academic advisors and university leadership in developing more efficient communication channels via a chatbot that can support the university's goals. [ABSTRACT FROM AUTHOR]

As colleges and universities strive to increase persistence and aid students in reaching graduation, they are utilizing alternative communication strategies like text messaging. Behavioral economics researchers suggest personalized, regular nudges can help college students make decisions that positively impact their college career and keep them on track for graduation. The current study presents the results of a randomized field experiment where a text messaging program was implemented in a large college at a public university. The intervention utilized a mixture of automated and personalized text messages from academic advisors and allowed for two-way communication between individual students and their major advisor. Mulitvariate analyses revealed the intervention had no impact on university persistence, but it did increase the odds of persisting in the college to the end of the semester, moving the average, overall college persistence rate from 93 to 95%. Effects were concentrated on underclass students, whose persistence rate moved from 87 to 93% at the college level. Underclass students also showed statistically significant university persistence effects, moving from 90 to 95%. Students who received texts but never engaged with the texting program were significantly less likely to request an advising appointment or to apply to be a student ambassador than were students in the control group. More research is needed to understand what motivates a student to engage with the texting software and to identify what the longer-term consequences of using text messaging to communicate with students might be. [ABSTRACT FROM AUTHOR]

Given gaps in both identifying and providing targeted interventions to struggling learners, the purpose of this study is to both improve rapid identification and to improve individualized academic advising for learners using this visual representation of performance. Across three graduating classes, individual growth curves were calculated for each student on National Board of Medical Examiners customized assessments during the pre-clerkship period using their deviation from the class average at each assessment point. These deviation scores were cumulatively summed over time and were regressed onto the sequence of exams. We analyzed the difference between the regression slopes of those students placed on Academic Probation (AP) versus not, as well as differences in slopes based on the timing of when a struggling learner was placed on AP to explore learner trajectory after identification. Students on AP had an average growth slope of − 6.06 compared to + 0.89 for those not on AP. Findings also suggested that students who were placed on AP early during pre-clerkship showed significant improvement (positive changes in trajectory) compared to students identified later in the curriculum. Our findings suggest that earlier academic probation and intervention with struggling learners may have a positive effect on academic trajectory. Future research can better explore how academic trajectory monitoring and performance review can be regularly used in advising sessions with students. [ABSTRACT FROM AUTHOR]

Academic advising is essential for students' success at the university level. This paper proposes a reusable and reconfigurable microservices-based platform called WS-ADVISING as a means of supporting effective academic advising efforts. WS-ADVISING will address several weaknesses related to the traditional academic advising process by offering enhanced and value-added online services that have proven beneficial for both advisors and students. These services will help advisors to better manage their large quotas of students while also helping students follow their academic plans more accurately and efficiently. WS-ADVISING has been developed and tested as an independent platform used to improve existing features offered by the Taibah University e-portal. In order to evaluate user satisfaction, two questionnaires were circulated among 200 undergraduate students and 36 advisors: the first collected an overview of users' perceptions of advising features currently offered by the Taibah University e-portal, while the second assessed the usability of the proposed WS-ADVISING platform. More than 77% of users surveyed stated that they were satisfied with WS-ADVISING and rated it as "effective"; meanwhile, over 81% also stated that they considered this platform an efficient solution for improved academic advising. [ABSTRACT FROM AUTHOR]

The present research work proposes the development of an integrated framework for the personalization and parameterization of learning pathways, aiming at optimizing the quality of the offered services by the Higher Educational Institutions (HEI). In order to achieve this goal, in addition to the educational part, the EDUC8 framework encloses the set of parameters that cover both the technical and the financial dimensions of a learning pathway, thus providing a complete tool for the optimization and calculation of the offered services by the HEIs in combination with the minimization of respective costs. Moreover, the proposed framework incorporates simulation modeling along with machine learning for the purpose of designing learning pathways and evaluating quality assurance indicators and the return on investment of implementation. The study presents a case study in relation to tertiary education in Greece, with a particular focus on Computer Science programs. Data clustering is specifically applied to learn potential insights pertaining to student characteristics, education factors and outcomes. Generally, the framework is conceived to provide a systematic approach for developing tertiary policies that help optimize the quality and cost of education. [ABSTRACT FROM AUTHOR]

One of the main challenges to be confronted by modern tertiary sector, so as to improve quality is the personalization of learning, which has to be combined with a minimization of the respective costs. However, personalization requires continuous reconfiguration of the academic plans since the academic status of each student, educational options and circumstances inside a Higher Educational Institution constantly change. In this paper, we present EDUC8 (EDUCATE) software environment that provides an integrated information technology solution concerning the dynamic recommendation and execution of personalized education processes. The implemented EDUC8 prototype aggregates a process execution engine, a rule engine and a semantic infrastructure for reconfiguring the learning pathways for each student. The semantic infrastructure consists of an ontology enclosing the required knowledge and a semantic rule-set. During the execution of learning pathways, the system reasons over the rules and reconfigures the next steps of the learning process. At the same time, new knowledge and facts originated from both the rule base and the learning pathway meta-models that are established during their execution are created, which constitute the evolving knowledge base of EDUC8 platform. The completeness and performance of the implemented infrastructure was tested for the modeling and selection of a set of appropriate academic recommendations regarding the Network Engineering specialization field of the Computer Science program. [ABSTRACT FROM AUTHOR]

Many degree-seeking college students struggle academically and ultimately never graduate. Academic challenges and persistence within the major are especially salient issues for students who major in science, technology, engineering, and mathematics. Academic probation serves as a means for informing students that they are at risk of dismissal, and many colleges and universities offer services to help students placed on probation to succeed academically. This paper presents two studies that examined the effectiveness of a goal-setting academic advising intervention for improving the grades of engineering students who were on academic probation; one study used a regression discontinuity design, and the other used an experimental design. The findings of both studies support the same overall conclusion: The intervention notably increased the grades of engineering students on probation who are beyond their first year of college, but it was not effective for students in their first year. This brief academic enhancement intervention appears to constitute a cost-effective strategy for bolstering the academic success of at-risk college students after their first year. [ABSTRACT FROM AUTHOR]

One of the main challenges to be confronted in Higher Education, so as to increase quality, is the personalization of education services, since each student constitutes a unique case. In this paper, we present the conceptualization of the domain of Learning Pathways in Higher Education. We present the EDUC8 (EDUCATE) ontology, which models the needed domain knowledge streams for the learning pathways and consists of four (4) main modules: 1) the learner model 2) the learning pathway model 3) the business model and 4) the quality assurance model. Taking into account the multifaceted nature of a learning pathway in a Higher Educational Institution (HEI), our proposal achieves a holistic conceptualization of the domain of educational provision, in order to be further utilized for the implementation of a Semantic Web Rules repository. This rule base is in control of the required streams of knowledge enclosed in the learning pathway for developing, recommending and executing tailored educational processes to meet each learner's personal characteristics. Finally, EDUC8 ontology is applied for the definition of a ruleset for the Computer Science program case study. [ABSTRACT FROM AUTHOR]

One of the main challenges to be confronted by Higher Educational Institutions (HEI), so as to increase quality, is the provision of personalized education services in a wide range of educational settings, often beyond the course sequences historically offered to students. However, this personalization requires the continuous reconfiguration and adaptation of the selected academic plans, since each student is a unique case and both the educational options and current circumstances inside an educational institution change rapidly. In this paper, we present an innovative software environment offered to the academic staff and personnel that provides an integrated information technology solution concerning the dynamic and personalized composition of students' learning pathways during execution phase. The software environment comprises a process execution engine based on a semantic infrastructure (ontology) for configuring the learning pathways. During the execution of learning pathways, the system reasons over the rules and dynamically recommends the next steps of the learning. The implemented graphical user interface and the respective business logic for the recommendation and execution of the learning pathway processes is presented, alongside with a graphical rule generator interface for the definition of the rule-set for the learning pathways in a user-friendly way. [ABSTRACT FROM AUTHOR]

In this article we report on our exploration of academic advising practices at 19 community colleges in the Florida College System after the implementation of Senate Bill 1720. This bill made developmental education optional for many students and mandated that colleges provide academic advising for all new students. Descriptive statistics of survey responses from college administrators uncovered academic advising patterns across these 19 community colleges. Our findings indicated that many administrators agreed that their advising practices were effective and that most colleges used a variety of advising tools. In an era of greater student choice, colleges diversified their advising protocols and methods of guiding students in a variety of ways. [ABSTRACT FROM AUTHOR]

To enhance student success, many colleges and universities have expanded academic support services and programmatic interventions. One popular measure that has been recognized as critical to student success is academic advising. Many institutions have expanded advising by creating centralized units staffed with professional advisors who serve specific student groups. In this study, I used propensity score matching to estimate the impact of using centralized academic advising at a large metropolitan public research university on undergraduate students' first-year GPA and second-year enrollment behavior. Using a cohort of 2,745 first-time full-time freshmen who matriculated in fall 2010, I matched students who used centralized advising with those who used no advising, over the course of two semesters. I then fit an OLS regression model to examine the impact of centralized advising on first-year GPA and a Zero Inflated Negative Binomial model to examine its impact on students' enrollment behavior in the second year. I used these parametric results to simulate average treatment effects. Results indicated that students who used centralized academic instead of no advising experienced an increase in their first-term GPA, second-term GPA, and first-year cumulative GPA. Also, students who used centralized advising during the second term experienced a decrease in their probability of first-year attrition. [ABSTRACT FROM AUTHOR]