Construction and Empirical Study of Learner Portrait in Online General Education Course

The problems of high course selection rate, low completion rate, and insufficient pertinence of learning support services in online general education courses are the focus of current general education researchers. Based on 3P (presage, process, and product) learning theory, we put forward a “three-stage, four-level” framework for learners’ portrait process of online general education course, including three learning stages of “presage process product” and four levels of “portrait goal, data collection, label analysis, and portrait service.” Then, taking the learners of the online general education course of Zhejiang Shuren College as an example, we make a case analysis based on the portrait framework, evaluate the learning effect from different stages, and put forward targeted teaching strategies and measures. Research results show that the proposed framework can reflect the characteristics of online learning experience, online learning investment, and online learning results of high-risk learners and can provide data support for the design of online learning support services and optimizing learning effects.

Course selection and specialisation

The work reported in the following pages was commenced early in 1953 but has extended over most of 1954 as well because of the time consumed in gathering some of the data. All the information presented refers to students of the Hawera Technical High School where the writer is one of the teaching staff. The purpose of this study was to examine the transition of pupils through the school and to try to evaluate some of the practices which have become established in providing for the education of those pupils.

Course selection and specialisation

The work reported in the following pages was commenced early in 1953 but has extended over most of 1954 as well because of the time consumed in gathering some of the data. All the information presented refers to students of the Hawera Technical High School where the writer is one of the teaching staff. The purpose of this study was to examine the transition of pupils through the school and to try to evaluate some of the practices which have become established in providing for the education of those pupils.

РОЗРОБКА АЛГОРИТМУ ЗАГАЛЬНОЇ СТРАТЕГІЇ АВТОМАТИЗАЦІЇ ВИБОРУ КУРСУ УХИЛЕННЯ СУДНА

During ships maneuvering, the main cause of accidents is that navigators do not always objectively assess the situation and may make wrong decisions on the identification situation of a dangerous approach and a collision hazard. The automation of the deviation course choice is considered as the automation avoidance process. Purpose. The article highlights the tasks of formalizing the deviation course as a value of automatic control, and proposes the development of an algorithm for the strategy of changing the course. Methodology. Conducted processing of practical data of own vessel. The existing models of the ship's movement were analyzes, as well as the systems for the automatic stabilization of the ship. To construct the algorithm, the COLREGs–72 was decomposed with respect to the belonging of the initial situation of the vessels to one of the areas of mutual obligations. Findings. According to the results of the given theme, an algorithm of the general strategy of choosing the course of evasion of the vessel was developed, considering the requirements of COLREG–72 rules. It were considered the types of control of automatic regulators and their limitations at the initial and final moments of the ship's turn. It was found out. It is necessary to create a multilevel mathematical description, which will include subsystems of different levels, to build a model of an integrated control system. Originality. It was determined the efficiency of using the principle of execution of ship’s turns by the method of observation with a given influence by analyzing the existing models of ship’s movement. It was offered to use the range of acceptable values of courses during the development of the model of automation ship avoidance process. Practical value. An algorithm for the general strategy of automation of the avoidance course selection was developed.

Deep Learning and Collaborative Filtering-Based Methods for Students’ Performance Prediction and Course Recommendation

At the beginning of a new semester, due to the limited understanding of the new courses, it is difficult for students to make predictive choices about the courses of the current semester. In order to help students solve this problem, this paper proposed a hybrid prediction model based on deep learning and collaborative filtering. The proposed model can automatically generate personalized suggestions about courses in the next semester to assist students in course selection. The two important tasks of this study are course recommendation and student ranking prediction. First, we use a user-based collaborative filtering model to give a list of recommended courses by calculating the similarity between users. Then, for the courses in the list, we use a hybrid prediction model to predict the student’s performance in each course, that is, ranking prediction. Finally, we will give a list of courses that the student is good at or not good at according to the predicted ranking of the courses. Our method is evaluated on students’ data from two departments of our university. Through experiments, we compared the hybrid prediction model with other nonhybrid models and confirmed the good effect of our model. By using our model, students can refer to the different recommendation lists given and choose courses that they may be interested in and good at. The proposed method can be widely applied in Internet of Things and industrial vocational learning systems.

A Step Backward and Forward in an Age-Friendly University Initiative: Adapting a Campus Learning Partnership

The 5-campus UMass system received designation as an Age-Friendly University (AFU) in 2019. AFU Principle 1 highlights the importance of involving older adults in University activities. UMass Lowell’s Center for Gerontology Research and Partnerships collaborated with the Learning in Retirement Association (LIRA) in Spring 2020 to offer aging-related courses around healthy aging. However, due to COVID-19, these were canceled and are re-scheduled for Spring 2021 via Zoom. The paper will describe the process of selecting course offerings with LIRA and the subsequent cancellation/rescheduling process and adaptation needed. A course will focus on AFU initiative and the opportunities and challenges at UMass Lowell. Likewise, the other course will offer a session on technology and aging where age-based digital divide and strategies for reducing it will be discussed. This paper will reflect on how the collaboration with LIRA and course selection process relates to the AFU principles 1, 5 and 9.

Investigating course choice motivations in university environments

Recommendation systems need a deeper understanding of users and their motivations to improve recommendation quality and provide more personalized suggestions. This is especially true in the education domain, the more about the student is known, the more useful recommendations can be made. However, although many studies on the course recommendation exist, studies on the students’ course selection motivations in universities are limited. This study investigates the factors that contribute to students’ choice when selecting courses in universities to better understand student perceptions, attitudes, and needs and leverage data-driven approaches for recommending and explaining the recommendations in university environments. A qualitative interview for university students (N = 10) comprised of open-ended questions as well as a questionnaire for students (N = 81) was conducted, aiming to investigate the main reasons behind their choices. The results of this study show that students highly value the course contents and the benefits of the course towards their future careers. Furthermore, students are influenced by other reasons such as the possibility of obtaining a higher grade, the popularity of professors, and recommendations from peers. Next, we extract the main categories of students’ motivations and analyzed the questionnaire data by employing statistical analysis methods as well as the k-means clustering algorithm to identify different types of students in terms of course selection. Based on our findings, we discuss implications for designing more personalized course recommendation systems.

The Effects of Academic Advising on Juniors and Seniors of English Department at Balkh University

Academic advising is the process between the students and academic advisors who exploring the value of a general education, reviewing the services and policies of the institution, discussing educational and career plans to make appropriate course selection goals for their students. Some studies have been conducted on academic advising and its effects on students’ academic development. Therefore, the present paper is an attempt to contribute the previous studies by presenting the factors that academic advising has an impact on students’ academic development. Thus, this paper will explore whether academic advising is efficient to the students or not. For this purpose, previous studies were reviewed, and the questionnaire was shaped. The participants were selected randomly who are the juniors and seniors (61% females and 39% males) of English Department at Balkh University. In the long run, the analysed data revealed that the functions of academic advising have impact on students’, success, development, educational outcomes, students’ satisfactions and students’ retention.