Learner Choice Research Articles

Digital well-being begins with inclusion: A systematic review of videoconferencing guidelines for equitable learning

As higher education institutions increasingly adopt videoconferencing technologies to broaden access to learning, the need for evidence-based, inclusive practices to support digital well-being becomes paramount. Integrating these technologies into the curriculum necessitates careful design considerations to prevent unintended consequences and uphold learners' privacy, safety, equity and humanity. Our systematic review, based on eight dimensions of digital wellness, has identified key inclusive design decisions for videoconference-enabled formal learning experiences. Drawing from data analysed from 36 empirical studies, we organised six inclusive design considerations for digital wellness in videoconferencing learning environments. These considerations – accessibility, active learning strategies, multimodal communication, readiness, social presence and sociocultural sensitivity – offer course designers a practical framework to create evidence-based practices that foster digital wellness and inclusion in videoconferencing learning spaces. Implications for practice or policy: Academic institutions should recognise digital wellness as a shared responsibility among institutional stakeholders, including faculty, learners, and administrative professionals. Institutional policies should prioritise learner choice and equitable access for co-creating knowledge and fostering safe communication. Stakeholders should be empowered to make informed choices about digital habits to mitigate unintended consequences and encourage mindful technology use. Accessibility barriers must be addressed through intentional learning design, ensuring meaningful participation and interaction for all.

Harmonizing Languages: The Impact of Multilingual Classroom Dynamics on Teaching Indonesian in Taiwan

This case study investigates the intricate interplay of multilingualism, specifically in Chinese and English, and its consequential impact on the language of instruction in the context of teaching Indonesian as a foreign language in Taiwan. This research delves into the nuances of language pedagogy within a multicultural and multilingual educational setting. This study employs case study methodology, gathering data through classroom observations and openended student questionnaires. Additionally, students' progress, as evidenced by assignments and feedback, serves as a quantitative indicator to measure the influence of multilingualism on their language learning journey. Key objectives include examining student preferences and perceptions on the use of Chinese and English in Indonesian language course instruction. The research explores instances of code-switching and code-mixing, shedding light on the dynamic language choices of instructors and learners. Through an analysis of student progress, the study aims to discern tangible outcomes associated with the multilingual approach. The results show that students found the classroom dynamics positive and motivating, enhancing their learning experience. Local students benefited from using Chinese, while international students appreciated the multilingual approach, though some noted occasional delays in translation. Overall, the study provides insights into the benefits and challenges of multilingualism in Indonesian language instruction in Taiwan, contributing to discussions on effective practices in multilingual education.

Does Autonomy Improve Satisfaction and Performance? A Case on Learner Choice

Autonomy has been identified as an essential attribute of learner-centered teaching. Allowing learners some choice regarding their learning can positively impact motivation and performance. Some concepts can be illustrated more clearly through film, including agricultural leadership concepts such as the stages of small group development. In this study, we examined differences between learners given autonomy to choose a film for an analysis essay assignment and learners not given a choice. Learners in two sections of an agricultural leadership course focusing on teams and group development were taught the stages of group development. Learners analyzed the development of a team in film. Additionally, learners responded to questions regarding their satisfaction with and perceptions of the assignment. Findings revealed that learners in both sections perceived the assignment as enjoyable. Learners in both groups indicated a preference for their respective treatment in future similar assignments. Both groups performed well on the assignment. Recommendations include replicating this study with an additional measure of motivation, a standardized measure for student satisfaction with learning, and a larger sample size. Given that both groups of learners indicated satisfaction with learning, we recommend instructors consider incorporating films or other media when possible and appropriate.

The Age of Screens: Investigating the Influence of Screen Media on the Language Choice and Identity Construction of Primary Grade Learners

The Age of Screens: Investigating the Influence of Screen Media on the Language Choice and Identity Construction of Primary Grade Learners

교육혁신을 위한 하이플렉스 수업 적용 사례 연구

As one of the teaching methods for educational innovation, the high-flex type of class, which allows flexible learning according to the needs of learners, has recently been attracting attention. The core values of hyflex classes are learner choice, equality, re-usability, and accessibility. Accordingly, in this study, as a way to innovate and improve the quality of university education at a time when it is necessary to develop a new teaching model to prepare for the expansion of online classes in universities and future education, we actually applied hyflex classes to learners at cyber universities. Examples of their learning experiences were analyzed. The research method was conducted using a semi-structured questionnaire and focusing on the learners’ hyflex learning experience through small group and individual interviews. The content of the learning experience interview was divided into 5 components and 12 sub-elements and analyzed as a meaning unit. Based on the research results, the strengths and weaknesses of hyflex classes and points of improvement were derived, and as a new teaching model for innovation in university education, hyflex classes were expanded in various ways and a teaching-learning strategy that could be effectively applied to classes was presented.

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Crowdsourcing has gradually become an effective e-government process to gather citizen complaints over the implementation of various public services. In practice, the collected complaints form a massive dataset, making it difficult for government officers to analyze the big data effectively. It is consequently vital to use data mining algorithms to classify the citizen complaint data for efficient follow-up actions. However, different classification algorithms produce varied classification accuracies. Thus, this study aimed to compare the accuracy of several classification algorithms on crowdsourced citizen complaint data. Taking the case of the LAKSA app in Tangerang City, Indonesia, this study included k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost for the accuracy assessment. The data were taken from crowdsourced citizen complaints submitted to the LAKSA app, including those aggregated from official social media channels, from May 2021 to April 2022. The results showed SVM with a linear kernel as the most accurate among the assessed algorithms (89.2%). In contrast, AdaBoost (base learner: Decision Trees) produced the lowest accuracy. Still, the accuracy levels of all algorithms varied in parallel to the amount of training data available for the actual classification categories. Overall, the assessments on all algorithms indicated that their accuracies were insignificantly different, with an overall variation of 4.3%. The AdaBoost-based classification, in particular, showed its large dependence on the choice of base learners. Looking at the method and results, this study contributes to e-government, data mining, and big data discourses. This research recommends that governments continuously conduct supervised training of classification algorithms over their crowdsourced citizen complaints to seek the highest accuracy possible, paving the way for smart and sustainable governance.

CRITERIA FOR DESIGNING AND EVALUATING THE QUALITY OF VIRTUAL CLASSROOMS DURING EMERGENCY LEARNING

Distance education during emergencies requires planning, design, and goal setting to create an effective learning environment. Virtual distance education involves more than just uploading educational content; it is rather an educational process that provides choice for learners, as well as flexibility and responsibility for learning and academic support. In this context, the aim of this research is to determine the quality criteria for designing virtual classrooms with their different styles (synchronous, asynchronous, and blended), and organizing them into categories and criteria to verify the availability of the criteria required for learning in the virtual environment. Also, the research aims to propose a method for evaluating and measuring the extent to which virtual classrooms during emergency learning meet its design quality criteria. The study used the descriptive method and analysis processes to determine the quality aspects of the virtual classrooms design, to draw out the design criteria and quality indicators, and to explore the opinions of the research population on the importance of these criteria and their measurement indicators. A purposive population of (17) specialists in the field of educational design and e-learning participated in the study, all of whom hold a PhD degree in the specialty in order to systematize the list of criteria for designing the virtual classroom and the indicators for measuring them in light of emergency learning. The importance of this current research lies in its aim to contribute to the improvement of training and learning environments through virtual classrooms during emergency learning, and to provide a list of design criteria that benefit teachers and instructional designers, in addition to reconsidering the use of learning management system tools and electronic content with virtual classrooms in order to achieve the maximum benefit for students in achievement outcomes of their learning, especially in the context of emergency learning.

Developing and Testing Audio Data Processing Modules in Python to Connect to and Data Be Scored by ASS Cloud Server

Automatic Speech Scoring (ASS) system developed on a basis of automatic speech recognition (ASR) technology is a powerful computer-assistant tool for oral test scoring. However, due to the limits of high equipment costs and high-tech operating costs of a local ASS, ASS cloud services have become the first choice of most oral English teachers and learners. The purpose of this paper is to develop and test modules in Python to preprocess the audio data, connect to the cloud server, and convert JSON data format into common Excel form. 1056 pieces of audio data were collected from test-takers’ read-aloud task of CEST-4 (College English Speaking Test band 4)) and six variables (i.e., “pronunciation”, “fluency”, “integrity”, “speed”, “duration”, and “overall”) were defined. After analyzing the data of the test results, it is found that the oral test score is mostly affected by the “pronunciation” and “integrity”, and the accuracy of pronunciation is the strongest predictor of oral performance. The modules and functions are helpful for teachers and students to use in daily oral test/practice, and these modules can also be employed in other second language oral test scored by ASS cloud sever, like oral Chinese test. Our results can provide reference and guidance for future oral research and teaching.

Survey Study of Clerkship Curriculum on Learner's Choice to Pursue Surgery: Positive Impact of Extracurricular Opportunities

Prior studies have focused on the role of the learning environment on students' decisions to pursue surgery, but few have analyzed the impact of the clerkship curriculum. This study assessed surgical clerkship curricula across United States (US) medical schools and their impact on students' likelihood of pursuing a surgical residency. A cross-sectional survey was developed to assess surgery clerkship characteristics. Questions included clerkship duration, number of offered and required surgical services, method of service assignment, and number of advanced clinical electives (e.g., fourth-year sub-internships) and additional surgical clinical opportunities (e.g., surgical elective rotations). Survey results were merged by the Association of American Medical Colleges with the percentages of students who matched into a surgical specialty. Linear regression models estimated the association of covariates with the percentage of students who (1) matched in surgical specialties, (2) were interested in surgery at medical school matriculation and ultimately matched into surgical residency (retention rate), and (3) were not interested in surgery at medical school matriculation but ultimately matched into surgical residency (recruitment rate). The survey was distributed to clerkship directors and coordinators at 66 medical schools through the Association for Surgical Education (ASE) from 5/1/2021 to 8/1/2021. All US medical schools in the ASE. A total of 21 medical schools responded (34.8% response rate). The overall retention rate was 36.4%, and the overall recruitment rate was 25.0%. Clerkships were 4 to 12 weeks. In 81% of programs, students submitted preferences and were assigned services. The percentage of students applying to surgical specialties was not associated with clerkship duration (p=0.79) or the number of required services (p=0.15), subspecialty services offered (p=0.33), or advanced clinical electives (p=0.24) but was associated with a program's having additional surgical clinical opportunities (p=0.02). Most of these factors were not associated with retention or recruitment rates. Offering more extracurricular surgical clinical opportunities was associated with having more students pursue surgical careers. Though limited by a relatively small sample size, our findings suggest that having shorter clerkships or limited subspecialty offerings may not have a significant influence on students' career choices.

BACK BACKGROUND, THEORETICAL BACKGROUND AND PRINCIPLES OF HEUTAGOGY

The article highlights the results of a theoretical study of the concept of heutagogy, its theoretical foundations and principles. Heutagogy is based on humanistic theory and approaches to learning, applying a holistic approach to the development of adult learners' abilities. It presents a new concept of learning, an innovative model of learning that meets the current requirements of the time, complex and dynamic changes that define our lives and are reflected in the need for continuous education and lifelong learning, with an emphasis on the requirement of an active and independent approach to these processes. It is proved that both heutagogy and andragogy emphasise self-directed and self-regulated learning. It has been found that heutagogy describes self-regulated learning as a holistic process that lasts throughout life. Learning is learner-centred and the learner is the best judge of their own learning needs. The needs of the learner are at the centre of the educational process. The main principles of heutagogy include: human activity (student centredness), non-linearity of learning, teachers focus on the learning process rather than content, learning goes beyond a specific discipline, learning takes place through the independent choice and self-directed action of an adult learner. It has been established that heutagogy focuses on the ability of adults to reproduce acquired knowledge and skills, to be able to use the competencies acquired in the learning process to solve new problems and to use these competencies effectively in unfamiliar contexts. Consider able attention is also paid to the ability to work creatively and the ability to cooperate in a team. The study of workplace learning that uses heutagogical learning strategies, such as action learning, coaching and mentoring, is promising.

Online learning resource recommendation method based on multi-similarity metric optimization under the COVID-19 epidemic

With the continuous COVID-19 pneumonia epidemic, online learning has become a normal choice for many learners. However, the problems of information overload and knowledge maze have been aggravated in the process of online learning. A learning resource recommendation method based on multi similarity measure optimization is proposed in this paper. We optimize the user score similarity by introducing information entropy, and use particle swarm optimization algorithm to determine the comprehensive similarity weight, and determine the nearest neighbor user with both score similarity and interest similarity through secondary screening in this method. The ultimate goal is to improve the accuracy of recommendation results, and help learners learn more effectively. We conduct experiments on public data sets. The experimental results show that the algorithm in this paper can significantly improve the recommendation accuracy on the basis of maintaining a stable recommendation coverage.

Learners’ career choices in STEM education: A review of empirical studies

The purpose of this study was to systematically review articles on the career choices of learners and STEM. The first goal was to classify research on career choice and STEM to identify trends and gaps in the literature. The second goal was to summarize the findings related to learners’ career choices at STEM and interpret them according to what has been reported in the literature. In this study, the PRISMA method was used to review articles in the educational literature STEM. Sixty-seven articles published in indexed journals in SCOPUS that met the specified criteria were analyzed systematically. The results showed that 51 of 67 articles were involved in the analysis to review by researchers. The results also showed that the earliest article that addressed career choice and STEM was published in 2011, and most of the articles studied were published after 2017. The results also showed that most articles on career choice in STEM were conducted in the United States, Israel, and Spain. The preferred sample groups in research on career choice in STEM fields are mostly high school and undergraduate students. In addition, the studies were broadly classified into five categories, including the effects of STEM activities and the effects on teachers, the effects of some variables on learners’ career choices, the study of learners’ career choices, and parental attitudes. Based on the results obtained from this research, implications are made for future research.

Video-Based (Livestream) and In-Person Continuing Professional Development: Participant and Course Features Associated With Choice.

Information on factors influencing learner choice between in-person and video-based continuing professional development (CPD) would help course leaders with planning and implementation. This study aimed to investigate how registration patterns differ between in-person and video-based offerings for the same CPD course. The authors obtained data from 55 CPD courses offered in-person (at various U.S. locations) and via livestreamed video, January 2020 to April 2022. Participants included physicians, advanced practice providers, allied health professionals, nurses, and pharmacists. Registration rates were compared by participant and course features including professional role, age, country of residence, distance to and perceived desirability of the in-person destination, and timing of registration. Analyses included 11,072 registrations, of which 4,336 (39.2%) were for video-based learning. There was significant heterogeneity in video-based registrations across courses, ranging 14.3% to 71.4%. Multivariable analysis showed higher video-based registrations for advanced practice providers (vs physicians; adjusted odds ratio [AOR] 1.80 [99% confidence interval, 1.55-2.10]), non-U.S. residents (AOR 3.26 [1.18-9.01]), longer distance (AOR 1.19 [1.16-1.23] for each doubling of distance), and courses in July-September 2021 (vs January-April 2022; AOR 1.59 [1.24-2.02]); video-based registrations were lower for current or former employees or trainees of our institution (AOR 0.53 [0.45-0.61]), destinations of moderate or high desirability (vs low; AOR 0.42 [0.34-0.51] and 0.44 [0.33-0.58], respectively), and early registration (AOR 0.67 [0.64-0.69] for each doubling of days between registration and course start). There was no significant difference by age (AOR 0.92 [0.82-1.05] for participants > 46 years old vs younger participants). The multivariable model correctly predicted actual registration 78.5% of the time. Video-based livestreamed CPD is popular (chosen by nearly 40% of participants), although choices varied widely by course. Professional role, institutional affiliation, distance traveled, location desirability, and registration timing have small but statistically significant associations with choice of video-based vs in-person CPD.

An investigation of self-regulated learning in a novel MOOC platform

Despite the proliferation of massive open online courses (MOOCs) and the impressive levels of enrolment they attract, many participants do not complete these courses. High drop-out has been identified as one of the major problems with existing MOOC formats. Our work addresses two factors relating to non-completion. Firstly, MOOCs require a high degree of self-regulated learning (SRL) skills but most do not adequately develop such skills, thus making them inaccessible in practice to many. Related to this is the inflexibility and passivity of many current MOOC formats, preventing individuals from setting their own learning objectives and directing their own learning. This paper presents preliminary findings from an investigation into MOOC learners’ SRL skills and the relationship to how participants learn. Following a design science methodology, we have developed a novel MOOC platform to support learner choice and to assist participants in defining learning goals and developing individual study paths. This paper describes the architecture of the system and presents findings from a pilot MOOC developed on the platform. Our results indicate that there is a high demand for more flexible, self-directed learning but that MOOC learners exhibit deficiencies in specific SRL skills including help seeking and task strategies. The contextualised nature of SRL skills means that even learners with a strong background of formal education may not deploy the best strategies for MOOC learning. This work is of significance to MOOC development in general as it highlights the need for targeted strategies to encourage SRL in MOOC platforms and innovation.

The Validity of Multiple Choice and Essay Composition Proficiency Assessment for English Language Learners

Students take assessment methods designed to examine their command of the English language in all four skill domains – reading, writing, speaking, and listening, as outlined by individual states. There has been much debate on whether multiple choice questions or essay compositions are valid in assessing students' English language proficiency. This paper aims to determine whether multiple-choice tests or written compositions are more reliable in gauging students' English competence. The author underlines that multiple choice tests could be successfully employed to test grammar, listening or reading skills. This research incorporated a qualitative methodology by utilizing deductive content analysis. The results of this study demonstrate that using multiple-choice questions is valid because they help students develop abilities for independent learning and raise their level of English proficiency. The deductive approach began with developing a theory that multiple choice questions are the most valid method of assessing a learner's proficiency in the English language. The author then found relevant and credible articles that address the research question. A hypothesis was then developed that teachers who use multiple choice questions can assess learners' English language proficiency compared to those who use compositions. Conclusions about the validity of the multiple choice questions are drawn and future research directions are also proposed.

VOCATIONAL INTERESTS OF ADOLESCENT LEARNERS IN RELATION TO FAMILY ENVIRONMENT AND ACADEMIC LEARNING ENVIRONMENT

The present study was an attempt to find the vocational interests of adolescent learner in relation to their family academic learning environment. Vocational interests and choices do not appear all of a sudden during adolescence, they appear as a result of developmental process.Normally the notion was there that vocational choices had their root in the family environment. To study the relevance of the impact of family on vocational choices this study was conducted. The Sample for the study was 200 adolescent learners from Fatehgarh Churian City. Percentage Analysis was done to analyze the vocational choices of the adolescent learners. Survey was conducted online to get the responses of the adolescent learners. The main finding of the study reflected that the influence on vocational choices had been shift from family influence towards Academic Learning Environment.

The Types of Learning Approaches Used by Engineering Students in Three Scenarios: An Adaptation of the R-SPQ-2F to China.

Deep learning is a type of high-level learning that has received widespread attention in research on higher education; however, learning scenarios as an important variable have been ignored to some extent in past studies. This study aimed to explore the learning state of engineering students in three learning scenarios: theoretical learning, experimental learning, and engineering practice. Samples of engineering university students in China were recruited online and offline; the students filled in the engineering Education-Study Process Questionnaire, which was revised from the R-SPQ-2F. The results of clustering analysis showed four types of learning approaches in the three scenarios: typical deep learning, typical shallow learning, deep-shallow learning, and free learning. Engineering learners in different learning scenarios tended to adopt different learning approaches and showed gender differences. Due to factors such as differences in culture and choice of learning opportunities, the deep and shallow learners demonstrated excellent learning performance, which is in sharp contrast with the “learning failure” exhibited by such students abroad.

Metamodeling for Policy Simulations with Multivariate Outcomes.

Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes. We combine 2 algorithm adaptation methods-multitarget stacking and regression chain with maximum correlation-with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings. Output variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving the interpretability of other models. In our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had a modest impact. Although advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.

Exploring the meaning of bilingual provision in the Further Education sector in Wales

ABSTRACT This paper explores the definition of ‘bilingual provision’ in vocational subjects in the Further Education sector in Wales, according to practitioners. The research is situated in the political context of the Welsh Government’s policy of increasing the number of Welsh speakers to one million by 2050. The research is based on a sample of four different areas across Wales and draws on semi-structured interviews with staff in schools and Further Education colleges. The study explores to what extent there is a consistent definition of bilingual provision. In the context of the debate regarding flexible and separate bilingualism, the article analyses the following components: teaching materials, delivery, assessment and bilingual teaching methods. It finds that there is an inconsistency in the definition of bilingual provision in the FE sector, driven by an emphasis on learner choice of language medium. The paper argues that there is a need to move away from this emphasis on ad hoc learner choice and offering provision, towards conscious language planning which will increase the number of learners following provision.

Continuous and Proactive Software Architecture Evaluation: An IoT Case

Design-time evaluation is essential to build the initial software architecture to be deployed. However, experts’ assumptions made at design-time are unlikely to remain true indefinitely in systems that are characterized by scale, hyperconnectivity, dynamism, and uncertainty in operations (e.g. IoT). Therefore, experts’ design-time decisions can be challenged at run-time. A continuous architecture evaluation that systematically assesses and intertwines design-time and run-time decisions is thus necessary. This paper proposes the first proactive approach to continuous architecture evaluation of the system leveraging the support of simulation. The approach evaluates software architectures by not only tracking their performance over time, but also forecasting their likely future performance through machine learning of simulated instances of the architecture. This enables architects to make cost-effective informed decisions on potential changes to the architecture. We perform an IoT case study to show how machine learning on simulated instances of architecture can fundamentally guide the continuous evaluation process and influence the outcome of architecture decisions. A series of experiments is conducted to demonstrate the applicability and effectiveness of the approach. We also provide the architect with recommendations on how to best benefit from the approach through choice of learners and input parameters, grounded on experimentation and evidence.