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Ali, El mezouary, Hmedna Brahim, and Omar Baz. "An Unsupervised Method for Discovering How Does Learners' Progress Toward Understanding in MOOCs." International Journal of Innovative Technology and Exploring Engineering 10, no. 5 (March 30, 2021): 40–49. http://dx.doi.org/10.35940/ijitee.e8673.0310521.
Повний текст джерелаАнотація:
Massive Open Online Course (MOOC) seems to expand access to education and it present too many advantages as: democratization of learning, openness to all and accessibility on a large scale, etc. However, this new phenomenon of open learning suffers from the lack of personalization; it is not easy to identify learners’ characteristics because their heterogeneous masse. Following the increasing adoption of learning styles as personalization criteria, it is possible to make learning process easier for learners. In this paper, we extracted features from learners' traces when they interact with the MOOC platform in order to identify learning styles in an automatic way. For this purpose, we adopted the Felder-Silverman Learning Style Model (FSLSM) and used an unsupervised clustering method. Finally, this solution was implemented to clustered learners based on their level of preference for the sequential/global dimension of FSLSM. Results indicated that, first: k-means is the best performing algorithm when it comes to the identification of learning styles; second: the majority of learners show strong and moderate sequential learning style preferences.
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Shkodina, Tatiana A. "Formation of an individual trajectory of online learning on the basis of cluster analysis." Journal Of Applied Informatics 18, no. 2 (March 31, 2023): 4–15. http://dx.doi.org/10.37791/2687-0649-2023-18-2-4-15.
Повний текст джерелаАнотація:
Justification for the relevance of developing an individual learning path in the field of online learning. The problems of forming an individual learning trajectory are analyzed. The main problem of personalization of learning from the point of view of the student is highlighted – the difficulty in finding the most appropriate sequence of studying educational objects that best suit their skills and preferences. It is concluded that the existing practices and methods of organizing a personalized educational process of courses in online learning are focused on the statistical characteristics of students that do not change during the study of an online course. Therefore, there is a need to develop a methodology for the formation of an individual learning path. The proposed approach allows us to consider the formation of recommendations as a dynamic process. An algorithm for the formation of an individual learning trajectory has been developed, which consists of a multi-criteria choice of a sequence of online courses at each moment of decision-making according to a given set of criteria and sequential mastering of skills. The choice of online courses is carried out using the cluster analysis method – k-means. Groups of clusters that meet the criteria of online courses have been identified. Each cluster consists of the closest objects – online courses. Based on these results, a sequential selection of online courses is made, using the available information about the user»s requirements and the skills that the learner needs to acquire. The purpose of developing for the formation of an individual learning trajectory is to provide students with the most appropriate sequence of learning objects in accordance with their skills and preferences.
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Zheng, Yujia, Siyi Liu, Zekun Li, and Shu Wu. "Cold-start Sequential Recommendation via Meta Learner." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4706–13. http://dx.doi.org/10.1609/aaai.v35i5.16601.
Повний текст джерелаАнотація:
This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online recommendation scenarios. However, most previous methods have trouble recommending cold-start items, which are prevalent in those scenarios. As there is generally no side information in sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available. Thus, we propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. This task is non-trivial as it targets at an important problem in a novel and challenging context. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user. Besides, our framework can be painlessly integrated with neural network-based models. Extensive experiments conducted on three real-world datasets verify the superiority of Mecos, with the average improvement up to 99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.
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Bilmona, Hanafi. "Sequential Blended Teaching Materials: Scaffolding Non-English Language Learners’ Scientific Literacy Using Online Sources, Edpuzzle." PEJLaC: Pattimura Excellence Journal of Language and Culture 1, no. 1 (June 1, 2021): 26–33. http://dx.doi.org/10.30598/pejlac.v1.i1.pp26-33.
Повний текст джерелаАнотація:
This study aimed to explore information from 44 Students of Primary Teacher Training Education Department as the sample of research. The 40 items of statement in questionnaires designed from positive point of view and scaled in Likert from; strongly agree (5) to strongly disagree (1). By the content area of two basic research questions in regard to Preferences and benefit for students in applying a sequential blended learning material in teaching English. Data was analyzed in statistic descriptive to get meaning. The result found that, there was 4,22% of students respond to items of questionnaires to more agree and strongly agree to the statements of questionnaires. This total average number of the respond of 44 students were found in 4,6816 % in questionnaires that represented preferences (13 items of statements) and 3,7727% in average found in statement of questionnaires that represent benefits (27 items of statements), meant there was positive respond toward this teaching approach and fixing to the preferences of student and scaffolded students’ other related scientific literacy.
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Jiang, Nan, Sheng Jin, Zhiyao Duan, and Changshui Zhang. "RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 710–18. http://dx.doi.org/10.1609/aaai.v34i01.5413.
Повний текст джерелаАнотація:
This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. Different from offline music generation and harmonization, online music accompaniment requires the algorithm to respond to human input and generate the machine counterpart in a sequential order. We cast this as a reinforcement learning problem, where the generation agent learns a policy to generate a musical note (action) based on previously generated context (state). The key of this algorithm is the well-functioning reward model. Instead of defining it using music composition rules, we learn this model from monophonic and polyphonic training data. This model considers the compatibility of the machine-generated note with both the machine-generated context and the human-generated context. Experiments show that this algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part. Subjective evaluations on preferences show that the proposed algorithm generates music pieces of higher quality than the baseline method.
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Thaipisutikul, Tipajin. "An Adaptive Temporal-Concept Drift Model for Sequential Recommendation." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 2 (June 11, 2022): 222–36. http://dx.doi.org/10.37936/ecti-cit.2022162.248019.
Повний текст джерелаАнотація:
Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.
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Thaipisutikul, Tipajin. "An Adaptive Temporal-Concept Drift Model for Sequential Recommendation." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 2 (June 7, 2022): 221–35. http://dx.doi.org/10.37936/ecticit.2022162.248019.
Повний текст джерелаАнотація:
Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.
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Kan, Yirong, Kun Yue, Hao Wu, Xiaodong Fu, and Zhengbao Sun. "Online Learning of Parameters for Modeling User Preference Based on Bayesian Network." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 02 (April 2022): 285–310. http://dx.doi.org/10.1142/s021848852250012x.
Повний текст джерелаАнотація:
By analyzing users’ behavior data for personalized services, most state-of-the-art methods for user preference modeling are often based on batch-mode machine learning algorithms, where all rating data are assumed to be available throughout the training process. However, data in the real world often arrives sequentially and user preference may change dynamically. The real-time characteristics of rating data make the algorithms for preference modeling challenging to suit real-world online applications. By the user preference model (UPM) based on Bayesian network with a latent variable (BNLV), uncertain relationships among relevant attributes of users, objects and ratings could be represented, in which user preference is represented by the latent variable. In this paper, we propose an online approach for parameter learning of UPM. Specifically, we first extend the classic Voting EM algorithm by using Bayesian estimation in terms of the situation with latent variables. Consequently, we propose the algorithm for learning parameters of UPM from few and sequentially-changing rating data to reflect the gradually changing preferences. Finally, we test the effectiveness of our proposed algorithm by conducting experiments on various datasets. Experimental results demonstrate the superiority of our method in various measurements.
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Li, Zhao, Long Zhang, Chenyi Lei, Xia Chen, Jianliang Gao, and Jun Gao. "Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation." Complexity 2020 (July 13, 2020): 1–13. http://dx.doi.org/10.1155/2020/6136095.
Повний текст джерелаАнотація:
Modeling user behaviors as sequential learning provides key advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for the purpose of personalized search and recommendation. Traditional methods for modeling sequential user behaviors usually depend on the premise of Markov processes, while recently recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences. In this paper, we propose integrating attention mechanism into RNNs for better modeling sequential user behaviors. Specifically, we design a network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions. Compared to previous works, our network can exploit the information of temporal dimension extracted by time interval-based GRU in addition to normal GRU to encoding user actions and has a specially designed matrix-form attention function to characterize both long-term preferences and short-term intents of users, while the attention-weighted features are finally decoded to predict the next user action. We have performed experiments on two well-known public datasets as well as a huge dataset built from real-world data of one of the largest online shopping websites. Experimental results show that the proposed ALI-GRU achieves significant improvement compared to state-of-the-art RNN-based methods. ALI-GRU is also adopted in a real-world application and results of the online A/B test further demonstrate its practical value.
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Anisa, Anisa. "EFL Students’ Perceptions and Preferences of The Video Use as a Replacement for Traditional Lecture Method." IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature 10, no. 1 (June 10, 2022): 310–25. http://dx.doi.org/10.24256/ideas.v10i1.2656.
Повний текст джерелаАнотація:
This study aimed to investigate students’ perceptions and preferences of video use as a replacement for traditional lecture during e-learning in times of the COVID-19 pandemic. This study used two-staged mixed-method. The design of this study was sequential explanatory. This study used 30 closed-ended questionnaires and focus group discussions as the instrument to gain a rich understanding of students’ video experiences, perceptions and preferences. Data were collected from 108 EFL students through an online questionnaire using Google form and face-to-face focus group discussion. Results show that (1) Most of students showed positive perceptions of the use of video as a replacement for traditional lecture during e-learning in times of the COVID-19 Pandemic. It can be seen from the questionnaire results that 83.3% of students showed positive perception. (2) Students mostly preferred video-based learning if there is post-videos watching activities such as reviewing, re-explaining, question and answer, etc. Furthermore, the types of videos they preferred are instructor-created videos and animation videos from YouTube
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Sztankay, Monika, Lisa M. Wintner, Sigrid Roggendorf, Thomas Nordhausen, Linda Dirven, Martin J. B. Taphoorn, Irma M. Verdonck-de Leeuw, et al. "Developing an e-learning course on the use of PRO measures in oncological practice: health care professionals’ preferences for learning content and methods." Supportive Care in Cancer 30, no. 3 (November 19, 2021): 2555–67. http://dx.doi.org/10.1007/s00520-021-06676-x.
Повний текст джерелаАнотація:
Abstract Purpose Implementation of patient-reported outcome measures (PROMs) in clinical routine requires knowledge and competences regarding their use. In order to facilitate implementation, an e-learning course for health care professionals (HCPs) on the utilisation of European Organisation for Research and Treatment of Cancer (EORTC) PROMs in oncological clinical practice is being developed. This study aimed to explore future users’ educational needs regarding content and learning methods. Methods The sequential mixed methods approach was applied. A scoping literature review informed the guideline for qualitative interviews with HCPs with diverse professional backgrounds in oncology and cancer advocates recruited using a purposive sampling strategy. An international online survey was conducted to validate the qualitative findings. Results Between December 2019 and May 2020, 73 interviews were conducted in 9 countries resulting in 8 topic areas (Basic information on PROs in clinical routine, Benefits of PRO assessments in clinical practice, Implementation of PRO assessments in clinical routine, Setup of PRO assessments for clinical application, Interpretation of PRO data, Integration of PROs into the communication with patients, Use of PROs in clinical practice, Self-management recommendations for patients based on PROs) subsequently presented in the online survey. The online survey (open between 3 June and 19 July 2020) was completed by 233 HCPs from 33 countries. The highest preference was indicated for content on interpretation of PRO data (97%), clinical benefits of assessing PRO data (95.3%) and implementation of routine PRO data assessment (94.8%). Regarding learning methods, participants indicated a high preference for practical examples that use a mixed approach of presentation (written, audio, video and interactive). Conclusion Educational needs for an integration of PROs in communication in clinical care and coherent implementation strategies became evident. These results inform the development of an e-learning course to support HCPs in the clinical use of EORTC PRO measures.
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Chaw, Lee Yen, and Chun Meng Tang. "Learner Characteristics and Learners’ Inclination towards Particular Learning Environments." Electronic Journal of e-Learning 21, no. 1 (January 27, 2023): 1–12. http://dx.doi.org/10.34190/ejel.21.1.2537.
Повний текст джерелаАнотація:
In addition to a face-to-face classroom learning environment, today’s learners in higher education are likely to have also experienced a blended learning or an online learning environment. These learning environments not only differ in their delivery modes, but also learning activities, class interactions, assessment approaches, etc. Learners tend to have differing perceptions about the effectiveness of different learning environments. This study therefore investigates whether the reasons learners like or dislike a learning environment reveal learner characteristics that may explain why some learners are more inclined towards a particular learning environment. This study also examines whether learner demographics influence learner characteristics and their preference for a particular learning environment. Using an exploratory sequential mixed methods research design, this study first conducted several focus group discussions and then administered an online questionnaire survey to collect input from students at a local university. Analyses derived four learner characteristics (i.e. desire for direct support, digital readiness, learning independence, and online hesitancy) based on the reasons why the students liked or disliked face-to-face classroom learning, blended learning, or online learning environments. A cluster analysis further distinguished the students into three groups (i.e. classroom learners, insecure learners, and online learners) based on the four learner characteristics. Analyses also found that learners’ demographics largely had no effect on learners’ characteristics and their preference for a particular learning environment. The findings suggest that learner characteristics may provide a clue to why certain learners have a preference for a face-to-face classroom learning, a blended learning, or an online learning environment. A better understanding of the relationship between learner characteristics and learners’ inclination towards a particular learning environment can be helpful to educational institutions and academics to design a range of engaging learning activities for learners with different characteristics.
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Halimi, Florentina, and Rina Halimi. "Pre-Service Teachers’ Perceptions on Active Learning Strategies in Online Classrooms." Journal of Educational and Social Research 12, no. 5 (September 2, 2022): 222. http://dx.doi.org/10.36941/jesr-2022-0136.
Повний текст джерелаАнотація:
Active learning is broadly viewed across a range of subjects, as a student-centered method for transmitting knowledge in a student-engaging manner. However, in an online setting, when instructors and students interact through chat boxes, breakout rooms, microphones, or web cameras, active learning can be applied in different ways. In order to gain an understanding of the influence of active learning used in online teaching and learning, this study was conducted with pre-service teachers from a private American university in Kuwait, selected by employing purposive sampling to get reliable data. An explanatory sequential mixed method design was used with an adopted Student Response to Instructional Practices (StRIP) instrument to collect quantitative data from 96 pre-service teachers. Interviews were conducted with 15 pre-service teachers to elicit information about their experiences regarding the use of active learning strategies introduced and practiced during semester-long online teaching and learning. In contrast to previous studies about students’ resistance to active learning, the current study points to a preference for the active learning method of instruction as a convenient approach that would provide an opportunity for all students in online classes to think and engage with course material and make the whole learning process more effective.
Received: 21 July 2022 / Accepted: 29 August 2022 / Published: 2 September 2022
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Xie, Ruobing, Shaoliang Zhang, Rui Wang, Feng Xia, and Leyu Lin. "Hierarchical Reinforcement Learning for Integrated Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4521–28. http://dx.doi.org/10.1609/aaai.v35i5.16580.
Повний текст джерелаАнотація:
Integrated recommendation aims to jointly recommend heterogeneous items in the main feed from different sources via multiple channels, which needs to capture user preferences on both item and channel levels. It has been widely used in practical systems by billions of users, while few works concentrate on the integrated recommendation systematically. In this work, we propose a novel Hierarchical reinforcement learning framework for integrated recommendation (HRL-Rec), which divides the integrated recommendation into two tasks to recommend channels and items sequentially. The low-level agent is a channel selector, which generates a personalized channel list. The high-level agent is an item recommender, which recommends specific items from heterogeneous channels under the channel constraints. We design various rewards for both recommendation accuracy and diversity, and propose four losses for fast and stable model convergence. We also conduct an online exploration for sufficient training. In experiments, we conduct extensive offline and online experiments on a billion-level real-world dataset to show the effectiveness of HRL-Rec. HRL-Rec has also been deployed on WeChat Top Stories, affecting millions of users. The source codes are released in https://github.com/modriczhang/HRL-Rec.
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Yu, Ke, and Monicah G Motlhabane. "WhatsApp’s Potential to Broaden Online Teaching and Learning: Perceptions of Undergraduate Students From One South African University." Journal of Information Technology Education: Research 21 (2022): 547–69. http://dx.doi.org/10.28945/5031.
Повний текст джерелаАнотація:
Aim/Purpose: Social media platforms have been increasingly incorporated into teaching and learning. However, studies using mixed methods to explore WhatsApp’s potential to broaden online teaching and learning remain limited. Background: This study reports the experiences and perspectives of undergraduate students in terms of their WhatsApp usage patterns and preferences during COVID-19 using a sequential mixed method. Methodology: Through a quantitative survey of undergraduate students from the Education Faculty in one South African university, quantitative data were collected from 92 participants. Qualitative interviews were followed with ten willing participants to further explore their perceptions and preference. Contribution: This study addresses the literature gap identified by Klein et al. (2018, p. 2) that “few studies that explore WhatsApp use in the natural environment of higher education” and the methodology gap Hashim identifies (2018) that the majority of the literature adopts a quantitative research methodology while only 10% use the mixed method. Our intention is set specifically on WhatsApp’s potential to broaden online teaching as the new norm beyond merely as a supplement teaching platform before COVID-19 or emergency remote teaching mode that WhatsApp serves since the onset of COVID-19. We triangulated the behaviors and perceptions of first-time WhatsApp users (scarcely separately discussed in the literature) and gender to ascertain lessons for more targeted strategies for more effective WhatsApp use. Another unique feature and novelty of this study is our separate analysis of active (e.g., initiating query or discussion) and passive use (e.g., receiving information). Findings: Our findings confirm that COVID-19 has accelerated universities’ digital transition as WhatsApp’s usage has undergone a great expansion from informal to formal spaces. However, informal use among students remains strong, particularly among first-time WhatsApp users. Communication remains one of the primary functions of WhatsApp in teaching and learning, but content-related functions and student discussion activities are clearly feasible and prevalent. However, passive use remains slightly more prevalent than active use even amongst frequent WhatsApp users. WhatsApp’s assignment-related usage is high but mainly limited to queries rather than assessment submissions or marking. Both WhatsApp’s usage and perceived usefulness has surpassed that of e-mail and of the university’s learning management system (LMS) where WhatsApp group functions seem to have contributed greatly to the perceived usefulness. Articulated advantages and challenges of WhatsApp largely corroborate with those identified in the literature, although our participants show some ambiguity concerning WhatsApp’s low cost as its main benefit. Recommendations for Practitioners: WhatsApp’s usages are versatile. So are its perceived benefits. However, practitioners need to consciously encourage its usage beyond passive use and also consider how WhatsApp can be incorporated into marking. Recommendation for Researchers: We found inconsistency regarding perceived benefits related to WhatsApp’s cost. Cost is important in low resource context and this inconsistency merits further examination. Our finding regarding WhatsApp’s limitation in terms of marking is not consistent with some literature. As marking functionality impacts broadening WhatsApp’s usage in teaching and learning, how WhatsApp has and potentially can be incorporated into LMS should be further explored. Impact on Society: WhatsApp has great potential to broaden online learning in higher education. However, it also has its limitation. This study demonstrates that WhatsApp can serve most teaching and learning functions in higher education. However, how these benefits and limitations impact different groups of users (e.g., 1st-time users, frequent users, gender, etc.) should be more consciously thought of, so is how more active use can be encouraged. Future Research: Further studies should examine whether the low cost is an important consideration in students’ preference for WhatsApp. Further studies should also explore how WhatsApp can be better used for marking.
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Monica Barbosu, Cabiria, Jose G. Perez-Ramos, Margaret Demment, Thomas Fogg, Jack Chang, Beatrice Aladin, Cheryl Smith, Timothy De Ver Dye, and Terry Doll. "2511." Journal of Clinical and Translational Science 1, S1 (September 2017): 51. http://dx.doi.org/10.1017/cts.2017.182.
Повний текст джерелаАнотація:
OBJECTIVES/SPECIFIC AIMS: The prevention, management, and treatment of HIV, STDs, and HCV requires continuous training that reflects contemporary best-practice and innovative care models. In order to improve the NYS AIDS Institute’s comprehensive web-enabled training program, which enhances the capacity of a diverse healthcare workforce, a needs assessment (NA) of our community of practice (CoP) is needed to better understand their training needs, circumstances, and instructional modalities preferences. The goal of the assessment was to better understand our CoP’s preferences of online trainings, and as a result to develop a “responsive design” system that will enhance user’s learning experience thus improving patient care. METHODS/STUDY POPULATION: We developed and deployed an NA survey using REDCap. The instrument consisted in 27 questions related to providers’ preferences on receiving continuing educational training and their use of technologies, including mobile platforms, online modules, webinars, and telehealth. As part of the recruitment strategy, several resources were deployed over a 1-month recruitment period including sequential email blasts, website promotion, and assessment links included in newsletters and social media. Weekly reminders were also used to promote the participation from our CoP. RESULTS/ANTICIPATED RESULTS: A total of 310 respondents participated in the NA, with 85.8% from NYS. 177 were clinicians (20.5% MD, 2.9% PA, 17.3% NP, and 16.3% RN) and 133 nonclinical providers (case/care managers, social workers, public health professionals, coordinators/administrators, and other). The participants worked in hospitals, community health centers, substance use centers, private practices, and state/local health departments. More than 90% of respondents indicated that they preferred both live/in-person and online training, and participants most strongly indicated that they stayed up-to-date on current developments through CDC, the AIDS Institute, and conferences. More than 60% of respondents considered that receiving CE credit for the training was very important and 28% indicated they would use training materials in Spanish if offered. In terms of technology, over 80% of the respondents preferred computers, but more 50% also used mobile devices (computer at home 61.8%, computer at work 85%, tablet 29.9%, iPhone 20.9%, Android 16.6%, other device 2.3%). DISCUSSION/SIGNIFICANCE OF IMPACT: Accessing an online CoP provided a useful opportunity to assess training needs and preferences of clinical and nonclinical providers. Most providers indicated that they were primarily likely to use a work computer to complete online training or secondarily a home computer. With a significant portion of respondents indicating use of tablets, smartphones, and other devices, online training opportunities should be developed with responsive design to assure flexibility and access. In addition to online training, participants indicated that they also strongly valued live, in-person training. Offering training with CDC and the NYS AIDS Institute branding, in Spanish, together with offering continuing education credit, were all seen as desirable training elements. Accessing this online CoP helped streamline and target training priorities and logistics.
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Nguyen, Lien, Hanna Jokimäki, Ismo Linnosmaa, Eirini-Christina Saloniki, Laurie Batchelder, Juliette Malley, Hui Lu, Peter Burge, Birgit Trukeschitz, and Julien Forder. "Do You Prefer Safety to Social Participation? Finnish Population-Based Preference Weights for the Adult Social Care Outcomes Toolkit (ASCOT) for Service Users." MDM Policy & Practice 6, no. 2 (July 2021): 238146832110279. http://dx.doi.org/10.1177/23814683211027902.
Повний текст джерелаАнотація:
Introduction. The Adult Social Care Outcomes Toolkit (ASCOT) was developed in England to measure people’s social care–related quality of life (SCRQoL). Objectives. The aim of this article is to estimate preference weights for the Finnish ASCOT for service users (ASCOT). In addition, we tested for learning and fatigue effects in the choice experiment used to elicit the preference weights. Methods. The analysis data ( n = 1000 individuals) were obtained from an online survey sample of the Finnish adult general population using gender, age, and region as quotas. The questionnaire included a best-worst scaling (BWS) experiment using ASCOT. Each respondent sequentially selected four alternatives (best, worst; second-best, second-worst) for eight BWS tasks ( n = 32,000 choice observations). A scale multinomial logit model was used to estimate the preference parameters and to test for fatigue and learning. Results. The most and least preferred attribute-levels were “I have as much control over my daily life as I want” and “I have no control over my daily life.” The preference weights were not on a cardinal scale. The ordering effect was related to the second-best choices. Learning effect was in the last four tasks. Conclusions. This study has developed a set of preference weights for the ASCOT instrument in Finland, which can be used for investigating outcomes of social care interventions on adult populations. The learning effect calls for the development of study designs that reduce possible bias relating to preference uncertainty at the beginning of sequential BWS tasks. It also supports the adaptation of a modelling strategy in which the sequence of tasks is explicitly modelled as a scale factor.
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Lin, Chih-Hao, and Yao-Yun Chang. "A Progressive Three-Stage Teaching Method Using Interactive Classroom Activities to Improve Learning Motivation in Computer Networking Courses." Sustainability 14, no. 9 (April 25, 2022): 5191. http://dx.doi.org/10.3390/su14095191.
Повний текст джерелаАнотація:
Generation Z students have their learning preferences. They like to learn independently, advocate for what they believe in, and work hard to achieve their goals. However, there are significant gaps between Generation Z students’ expectations for learning and prior experiences, especially for three domains of motivation in online learning environments: relatability, affirmation, and opportunity. This study aims at exploring the effectiveness of a progressive teaching method designed for Generation Z students in computer networking courses. This study proposes a progressive three-stage teaching method that gradually implements traditional lecture, individual flipped learning, and cooperative flipped learning methods over a semester. The design principle of this study differs from most existing studies that focus on the effectiveness of specific teaching methods. This study encourages each student to learn sequentially through three teaching stages. The purpose of this study is to investigate the changes in students’ learning experiences, particularly in terms of learning comprehension and learning motivation. The research results show that the proposed progressive teaching method can improve students’ understanding of computer networking courses and enhance their learning motivation. Participants agreed that the proposed progressive pedagogy can improve their teamwork skills and provide a different learning experience in the computer networking courses.
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Lin, Chih-Hao, and Yao-Yun Chang. "A Progressive Three-Stage Teaching Method Using Interactive Classroom Activities to Improve Learning Motivation in Computer Networking Courses." Sustainability 14, no. 9 (April 25, 2022): 5191. http://dx.doi.org/10.3390/su14095191.
Повний текст джерелаАнотація:
Generation Z students have their learning preferences. They like to learn independently, advocate for what they believe in, and work hard to achieve their goals. However, there are significant gaps between Generation Z students’ expectations for learning and prior experiences, especially for three domains of motivation in online learning environments: relatability, affirmation, and opportunity. This study aims at exploring the effectiveness of a progressive teaching method designed for Generation Z students in computer networking courses. This study proposes a progressive three-stage teaching method that gradually implements traditional lecture, individual flipped learning, and cooperative flipped learning methods over a semester. The design principle of this study differs from most existing studies that focus on the effectiveness of specific teaching methods. This study encourages each student to learn sequentially through three teaching stages. The purpose of this study is to investigate the changes in students’ learning experiences, particularly in terms of learning comprehension and learning motivation. The research results show that the proposed progressive teaching method can improve students’ understanding of computer networking courses and enhance their learning motivation. Participants agreed that the proposed progressive pedagogy can improve their teamwork skills and provide a different learning experience in the computer networking courses.
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Lin, Chih-Hao, and Yao-Yun Chang. "A Progressive Three-Stage Teaching Method Using Interactive Classroom Activities to Improve Learning Motivation in Computer Networking Courses." Sustainability 14, no. 9 (April 25, 2022): 5191. http://dx.doi.org/10.3390/su14095191.
Повний текст джерелаАнотація:
Generation Z students have their learning preferences. They like to learn independently, advocate for what they believe in, and work hard to achieve their goals. However, there are significant gaps between Generation Z students’ expectations for learning and prior experiences, especially for three domains of motivation in online learning environments: relatability, affirmation, and opportunity. This study aims at exploring the effectiveness of a progressive teaching method designed for Generation Z students in computer networking courses. This study proposes a progressive three-stage teaching method that gradually implements traditional lecture, individual flipped learning, and cooperative flipped learning methods over a semester. The design principle of this study differs from most existing studies that focus on the effectiveness of specific teaching methods. This study encourages each student to learn sequentially through three teaching stages. The purpose of this study is to investigate the changes in students’ learning experiences, particularly in terms of learning comprehension and learning motivation. The research results show that the proposed progressive teaching method can improve students’ understanding of computer networking courses and enhance their learning motivation. Participants agreed that the proposed progressive pedagogy can improve their teamwork skills and provide a different learning experience in the computer networking courses.
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Branco-Illodo, Ines, Teresa Heath, and Caroline Tynan. "“You really shouldn't have!” Coping with failed gift experiences." European Journal of Marketing 54, no. 4 (March 7, 2020): 857–83. http://dx.doi.org/10.1108/ejm-05-2018-0309.
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Purpose This paper aims to examine coping approaches used by receivers to deal with failed gift experiences, thereby dealing with misperceptions between givers and receivers that could affect their relationship. Design/methodology/approach This study uses a sequential, multimethod methodology using background questionnaires, online diary method and 27 semi-structured interviews. Findings Receivers cope with failed gift experiences through concealing, disclosing or re-evaluating the gift experience. These approaches encompass several coping strategies, allowing receivers to deal with their experiences in ways that help them manage their relationships with givers. Research limitations/implications Informants described gift experiences in their own terms without being prompted to talk about coping, thus some insights of coping with failed gifts may have been missed. Multiple data collection methods were used to minimise this limitation, and the research findings suggest new avenues for future research. Practical implications The present research helps retailers and brands to minimise gift failure by promoting gifts that emphasise aspects of the giver–receiver relationship, assists givers in their learning from gift failure by making them aware of the receiver’s preferences and reduces the cost of gift failure by offering further opportunities to dispose of unwanted gifts. Originality/value This paper contributes to the emerging topic of consumer coping by providing a novel and rounded understanding of coping in the context of failed gift events, identifying new reasons for gift failure, highlighting receivers’ ethical considerations when responding to failed gifts and proposing new insights for the coping literature.
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Khan, Qudsia Umaira, Hameyl Tahir, and Abdur Rafae Ahmad. "Perception about Teaching and Learning Methodologies Applied in Physiology: A study on Medical Students of Pakistan." Pakistan Journal of Medical and Health Sciences 15, no. 9 (September 30, 2021): 2207–11. http://dx.doi.org/10.53350/pjmhs211592207.
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Background: The study of physiology is an essential part of the medical school curriculum. Medical teachers have identified the preference for a specific mode of content delivery to communicate knowledge to students in a rational, strategic, coherent, and sequential manner. In comparison to the focus on systems-based didactic lectures, more emphasis is now put on the developing critical thinking skills. Physiology is widely acknowledged as a difficult subject for medical students to grasp, incorporate, and apply in clinical sciences. Aim: To learn about students’ perceptions of teaching, learning, and assessment approaches used in the physiology. Method A quantitative cross-sectional survey was conducted online on 533 medical students from first to final year and also post grate students. After the approval of Ethical review committee, a questionnaire to determine the various aspects of Physiology as a subject being taught. The survey was conducted online via “Google forms''. Participants answered anonymously with informed consent, and the survey was conducted for a period of two months. Data was analyzed using SPSS version 23. Results: A total number of 533 students participated in this research and responding to Physiology learning and teaching. When students were asked about that which subject is most interesting in first Year MBBS, majority of the students that is 46.2% of the responses claimed that Physiology is the most interesting subject. 33.6% students were from 1st Year. 9.2% students were Postgraduates. 63.4% of the students preferred studying physiology from Guyton and Hall as reference book for Physiology. Majority of students that is 25.5% of the students found Blood Physiology to be the most interesting. Whereas 19.8% found Heart or Cardio Vascular System Physiology as the most interesting subject.15.6% found cell and nerve muscle as an interesting topic in Physiology.9.6% found Endocrinology and reproduction physiology as interesting as compared to 7.7% who found central Nervous system Physiology interesting. 43.9% of the students responded that they perfeer face to face interactive lectures.23.3% of the students perfered small group discussion.10.9% students prefered learing by tutorilas.Interestingly, 20.4% of the participants replied that they would definitely pursue physiology as their career. Conclusion: Physiology is the most interesting subject preferred by majority of students. Keywords: Perception, learning methodologies, medical students
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Bishop, Nicholas, Hau Chan, Debmalya Mandal, and Long Tran-Thanh. "Sequential Blocked Matching." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 4834–42. http://dx.doi.org/10.1609/aaai.v36i5.20411.
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We consider a sequential blocked matching (SBM) model where strategic agents repeatedly report ordinal preferences over a set of services to a central planner. The planner's goal is to elicit agents' true preferences and design a policy that matches services to agents in order to maximize the expected social welfare with the added constraint that each matched service can be blocked or unavailable for a number of time periods. Naturally, SBM models the repeated allocation of reusable services to a set of agents where each allocated service becomes unavailable for a fixed duration. We first consider the offline SBM setting, where the strategic agents are aware of their true preferences. We measure the performance of any policy by distortion, the worst-case multiplicative approximation guaranteed by any policy. For the setting with s services, we establish lower bounds of Ω(s) and Ω(√s) on the distortions of any deterministic and randomised mechanisms, respectively. We complement these results by providing approximately truthful, measured by incentive ratio, deterministic and randomised policies based on random serial dictatorship which match our lower bounds. Our results show that there is a significant improvement if one considers the class of randomised policies. Finally, we consider the online SBM setting with bandit feedback where each agent is initially unaware of her true preferences, and the planner must facilitate each agent in the learning of their preferences through the matching of services over time. We design an approximately truthful mechanism based on the explore-then-commit paradigm, which achieves logarithmic dynamic approximate regret.
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Jain, Ashesh, Shikhar Sharma, Thorsten Joachims, and Ashutosh Saxena. "Learning preferences for manipulation tasks from online coactive feedback." International Journal of Robotics Research 34, no. 10 (May 26, 2015): 1296–313. http://dx.doi.org/10.1177/0278364915581193.
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Milić, Nataša, Andrija Pavlović, and Valerija Janićijević. "How can we teach our students if we do not know how they learn? Medical students' learning styles and academic performance." Inovacije u nastavi 36, no. 2 (2023): 48–59. http://dx.doi.org/10.5937/inovacije2302048m.
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Circumstances arising after the proclamation of the COVID-19 pandemic indicate the need for a permanent change in the access to education in medicine, the use of online tools and flexibility in the application of innovative learning solutions. This study aimed to determine medical students' learning styles and to use this information to improve distance learning platforms in order to promote personalized learning performance. A prospective cohort study was conducted among medical students attending the Faculty of Medicine, University of Belgrade, who were enrolled in the obligatory Medical statistics and informatics (MSI) course during 2017-18 school year. The Index of Learning Styles (ILS) questionnaire was used to measure the dimensions of learning styles: Sensing/Intuitive, Visual/Verbal, Active/Reflective, and Sequential/Global. Additional data included demographic information and formal evaluation of student achievements. The existing online teaching approach supported by Moodle LMS was upgraded for upcoming 2020-21 school year to cover all student learning preferences. Four hundred sixty-two medical students were enrolled. Most students were female (64.5%); average age 21.4±1.1 years. The average problem solving and final statistics scores were 16.8±2.6 and 82.8±12.4, respectively. The dominant learning styles on the Active/Reflective and Sensing/Intuitive scales were active (74.9%) and sensing (50%). On the Visual/Verbal and Sequential/Global scales main learning preferences were neutral to visual (48.5% and 41.3%, respectively) and neutral to sequential (72.3% and 18.4%, respectively). The strong sensing learning style and age were significant predictors in multivariate regression models, with problem solving and final statistics score as dependent variables. Based on these findings, the existing learning platform has been upgraded to cover all learning preferences and personalize learning for students with learning styles other than sensing. Students with a strong sensing learning preference have a better academic performance in MSI. Better knowledge and understanding of students learning styles can aid instructors and curriculum designers to adjust teaching methods in order to help students gain their full academic potential during COVID-19 pandemic.
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Muhfahroyin, Muhfahroyin, and Agus Sujarwanta. "Student’s commitments and preferences in online learning." Journal of Education and Learning (EduLearn) 17, no. 2 (May 1, 2023): 326–34. http://dx.doi.org/10.11591/edulearn.v17i2.20698.
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During the COVID-19 pandemic, the learning was conducted by online system. The objective of the research was to understand the commitments and preferences of students in online learning. A total of 516 students participated in filling out an online questionnaire. The data were analysed descriptively referring to the critical success factors (CSFs). Based on the research result, there were three most dominant obstacles, they were: i) Internet interference (42.71%); ii) Limited quota (24.49%); and iii) Other activities (22.92%). There were students who did not attend full-time (22.45%). The weak commitment was boredom (2.04%) and feeling that they understood the module (5.10%). There were students who do not study full-time, only filling out the attendance list (53.1%). A small number of students (2.04%) fill out the attendance list and upload assignments as a top priority. According to this research, 46.7% of students liked online learning and 39.7% did not like it. In addition, 42.83% of students were bored. It means that about 60% of saturation comes from like-dislike preferences. The remaining 40% are influenced by other factors. The students’ commitments and preferences are influenced by many factors. Those who force themselves to learn ineffectively will be at risk of learning loss.
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Sakagami, Hidekazu, and Tomonari Kamba. "Learning personal preferences on online newspaper articles from user behaviors." Computer Networks and ISDN Systems 29, no. 8-13 (September 1997): 1447–55. http://dx.doi.org/10.1016/s0169-7552(97)00016-0.
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Huertas-Abril, Cristina A., and Barbara Muszyńska. "Learning Design Preferences in LMOOCs." International Journal of Computer-Assisted Language Learning and Teaching 12, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/ijcallt.291106.
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Massive Open Online Courses (MOOCs) have come to stay supported by the development of educational technologies, and within them Language MOOCs (LMOOCs) are a phenomenon that has risen expectations but also shown their limitations. This study aims at comparing students’ preferences from two universities (UCO, Spain, and ULS, Poland) regarding the learning design preferences of LMOOCs and analyze whether there are differences based on sociocultural context, gender and educational stage. The respondents (n = 260) stated to be in favor of a balance between the constructivist and instructionist educational practices in online language courses. The findings reveal significant differences regarding LMOOCs learning and feedback design in terms of gender and sociocultural context, while no significant differences were found between undergraduate and postgraduate students. These results may be used to plan innovative and effective learning situations that suit learners’ needs and preferences, which should lead to higher quality of learning, and higher learner engagement and satisfaction.
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Wang, Xuebin, Zhengzhou Zhu, Jiaqi Yu, Ruofei Zhu, DeQi Li, and Qun Guo. "A learning resource recommendation algorithm based on online learning sequential behavior." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (March 2019): 1940001. http://dx.doi.org/10.1142/s0219691319400010.
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The accuracy of learning resource recommendation is crucial to realizing precise teaching and personalized learning. We propose a novel collaborative filtering recommendation algorithm based on the student’s online learning sequential behavior to improve the accuracy of learning resources recommendation. First, we extract the student’s learning events from his/her online learning process. Then each student’s learning events are selected as the basic analysis unit to extract the feature sequential behavior sequence that represents the student’s learning behavioral characteristics. Then the extracted feature sequential behavior sequence generates the student’s feature vector. Moreover, we improve the H-[Formula: see text] clustering algorithm that clusters the students who have similar learning behavior. Finally, we recommend learning resources to the students combine similarity user clusters with the traditional collaborative filtering algorithm based on user. The experiment shows that the proposed algorithm improved the accuracy rate by 110% and recall rate by 40% compared with the traditional user-based collaborative filtering algorithm.
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Anand Jain, Shubham, Shreyas Goenka, Divyam Bapna, Nikhil Karamchandani, and Jayakrishnan Nair. "Sequential community mode estimation." ACM SIGMETRICS Performance Evaluation Review 49, no. 3 (March 22, 2022): 63–64. http://dx.doi.org/10.1145/3529113.3529135.
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Several applications in online learning involve sequential sampling/polling of an underlying population. A classical learning task in this space is online cardinality estimation, where the goal is to estimate the size of a set by sequential sampling of elements from the set (see, for example, [2,4,7]). The key idea here is to use 'collisions,' i.e., instances where the same element is sampled more than once, to estimate the size of the set. Another recent application is community exploration, where the goal of the learning agent is to sample as many distinct elements as possible, given a family of sampling distributions/domains to poll from (see [3, 6]).
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Augustine, Grecilda, Aisyah Nazamud-din, and Lisbeth Sinan Lendik. "Lessons from Covid-19 Pandemic: Students’ Remote Learning Preferences in Malaysia." Malaysian Journal of Social Sciences and Humanities (MJSSH) 7, no. 5 (May 23, 2022): e001521. http://dx.doi.org/10.47405/mjssh.v7i5.1521.
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The unexpectedly disruptive period is currently being reinforced with improved ideas and approaches to meet the needs of the pupils. With pandemic tiredness and digital exhaustion being highlighted, a better understanding of students' learning preferences must be updated regularly during unprecedented times. This mixed-method study seeks to identify and explore students’ preferences based on their remote learning experiences in a public university in Malaysia. An online survey was used to gather the data for the analysis to identify the students’ preferences over the platform used, assessment types, and schedule preferences. In addition, the students’ suggestions were gathered to get more insights from their perspectives. The results revealed students preferred Google Meet, WhatsApp Messenger, and Google Classroom as the medium for delivery. On the other hand, the students favoured shorter time spent and early time slots for online class scheduling. They also preferred quizzes as the most preferred type of assessment. Lastly, students suggested considering various factors to conduct successful remote learning namely empathy from the lecturer, student engagement, students’ readiness, students’ accessibility, content delivery, flexibility, and motivation. Conclusively, the implication of this study will contribute to the body of the literature on remote learning during a pandemic. Moreover, educators in tertiary education could utilise the students’ preferences as feedback to enhance their teaching and learning delivery during remote learning. This study was limited by the absence of lecturers’ preferences and suggestions. Future studies that investigate other perspectives could create a common ground between educators and learners.
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Mohd Radzi, Nor Azira, Nor Alifah Rosaidi, Noorazalia Izha Haron, and Azhar Abdul Rahman. "STUDENTS' PREFERENCES IN ONLINE DISTANCE LEARNING DURING COVID-19." International Journal of Education, Psychology and Counseling 7, no. 47 (September 5, 2022): 354–63. http://dx.doi.org/10.35631/ijepc.747030.
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The educational community is in shambles as a result of the COVID-19 pandemic. Pre-diploma students have particular challenges since they are unfamiliar with higher education, lack the tools necessary for efficient online distance learning, and are not academically driven. Pre-diploma UiTM students are primarily from low-income families. This aggravates problems with their online learning environment. University students must be independent learners due to the high demand for online education when the epidemic occurs. This study aims to look into 35 pre-diploma students' views on their autonomy in online English classes. The information was gathered near the end of the semester. Students asserted that their level of control over their online learning was minimal at the start of the semester, as was to be expected. They spent weeks trying to take control of their academic lives. The students discovered, however, that by the seventh week, they had more control over their online learning, indicating that they were beginning to understand what was happening in their virtual English classrooms. Once they understood that this is the only way to learn during the pandemic, they felt more at ease with their liberty in learning. This study also discovered that these brand-new undergrads positively embraced and adopted online learning as a novel method of training.
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Bak, Carsten Kronborg, and Simon Schulin. "University College Students’ Experiences with Online Teaching One Year after COVID-19 Lockdown in Spring 2020." Social Sciences 12, no. 3 (March 6, 2023): 156. http://dx.doi.org/10.3390/socsci12030156.
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The aim of this study was to explore university college students’ experiences with online teaching one year after the lockdown in spring 2021.With quantitative cluster analysis, we have identified a “learning gradient” among students, showing that cluster 1 students have the most positive preferences towards online teaching and the highest degree of self-regulation and learning outcome, cluster 2 students are mixed (both positive and negative experiences), and cluster 3 students have the most negative preferences and the lowest self-regulation and learning outcome. In this study, we used 5 focus group interviews with 29 students based on their preferences towards online teaching to discuss and reflect on their own study planning, the shift in the learning environment, their perceived learning outcome, and positive and negative experiences from online teaching. The results from this study have shown that students’ self-regulated learning strategies during online teaching environment are important for their learning outcome. Thus, we demonstrate the disjunction between students’ learning outcome and the classroom as a fixed place for learning.
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AL-Khaleefa, Ahmed, Mohd Ahmad, Azmi Isa, Mona Esa, Ahmed AL-Saffar, and Mustafa Hassan. "Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine." Applied Sciences 9, no. 5 (March 2, 2019): 895. http://dx.doi.org/10.3390/app9050895.
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Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively.
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Gupta, Shalini, and Veer Sain Dixit. "A Meta-Heuristic Algorithm Approximating Optimized Recommendations for E-Commerce Business Promotions." International Journal of Information Technology Project Management 11, no. 2 (April 2020): 23–49. http://dx.doi.org/10.4018/ijitpm.2020040103.
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To provide personalized services such as online-product recommendations, it is usually necessary to model clickstream behavior of users if implicit preferences are taken into account. To accomplish this, web log mining is a promising approach that mines clickstream sessions and depicts frequent sequential paths that a customer follows while browsing e-commerce websites. Strong attributes are identified from the navigation behavior of users. These attributes reflect absolute preference (AP) of the customer towards a product viewed. The preferences are obtained only for the products clicked. These preferences are further refined by calculating the sequential preference (SP) of the user for the products. This paper proposes an intelligent recommender system known as SAPRS (sequential absolute preference-based recommender system) that embed these two approaches that are integrated to improve the quality of recommendation. The performance is evaluated using information retrieval methods. Extensive experiments were carried out to evaluate the proposed approach against state-of-the-art methods.
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Wiguna, I. Gusti Ngurah Wira. "Students’ Preferences Toward Virtual, Classroom, and Blended Learning." Journal of Educational Study 2, no. 1 (February 15, 2022): 65–71. http://dx.doi.org/10.36663/joes.v2i1.262.
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This research aims to find out the learning system that students want after the COVID-19 pandemic, and the reason they chose the learning system. This study uses various survey methods. The subject of this study was all 11th grade students at SMA Negeri 2 Negara. This study uses google form questionnaires as research instruments. The research instrument consists of 11 indicators divided into 48 statements. The data obtained from the questionnaire are the student's response, which then the number of responses is summed and divided to find the average score of the student's response for each learning system. Scores from each learning system (virtual/online, classroom/face-to-face, and blended) are compared. The results of the study showed that students in SMA Negeri 2 Negara prefer to learn with a face-to-face learning system compared to blended learning systems and online / virtual learning systems. The reason students in SMA Negeri 2 Negara prefer face to face learning is because they can understand the material better through direct interaction with teachers and students.
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Kale, Archana P., Sumedh Sonawane, Revati M. Wahul, and Manisha A. Dudhedia. "Improved Genetic Optimized Feature Selection for Online Sequential Extreme Learning Machine." Ingénierie des systèmes d information 27, no. 5 (October 31, 2022): 843–48. http://dx.doi.org/10.18280/isi.270519.
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Extreme learning machine (ELM) is a rapid classifier, evolved for batch learning mode which is not suitable for sequential input. As retrieving of data from new inventory which is leads to time extended process. Therefore, online sequential ELM (OSELM) algorithm is progressed to handle the sequential input in which data is read 1 by 1 or chunk by chunk mode. The overall system generalization performance may devalue because of the amalgamation of random initialization of OS-ELM and the presence of redundant and irrelevant features. To resolve the said problem, this paper proposes a correspondence improved genetic optimized feature selection paradigm for sequential input (IG-OSELM) for radial basis or function by using clinical datasets. For performance comparison, the proposed paradigm experimented and evaluated for ELM, improved genetic optimized for ELM classifier (IG-ELM), OS-ELM, IG-OSELM. Experimental results are calculated and analyzed accordingly. The comparative results analysis illustrates that IG-ELM provides 10.94% improved accuracy with 43.25% features as compared to ELM.
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Wang, Xingbiao, Bin Gu, Quanyi Zou, and Rui Lei. "Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine." Security and Communication Networks 2022 (May 11, 2022): 1–15. http://dx.doi.org/10.1155/2022/1735573.
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Due to the limited number of labeled samples, semisupervised learning often leads to a considerable empirical distribution mismatch between labeled samples and unlabeled samples. To this end, this paper proposes a novel semisupervised algorithm named Local Gravitation-based Semisupervised Online Sequential Extreme Learning Machine (LGS-OSELM), learning to unlabeled samples follows from easy to difficult. Each sample is formulated as an object with mass and associated with local gravitation generated from its neighbors. The similarity between samples is measurable by the local gravitation measures (centrality CE and coordination CO). First, the LGS-OSELM uses the labeled samples to learn the initialization model by implementing ELM. Second, the unlabeled samples with a high confidence level that is easy to learn are labeled with the pseudo label. Then, these samples are utilized to iterate the neural network by implementing OS-ELM. The proposed approach ultimately realizes effective learning of all samples through successive learning unlabeled samples and iterating neural networks. We implement experiments on several standard benchmark data sets to verify the performance of the proposed LGS-OSELM, which demonstrates that our proposed approach outperforms state-of-the-art methods in terms of accuracy.
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Abdur Rohim, Agil. "Indonesian Efl Students’ Perception Towards Online Learning: Voices From Freshmen." IJET (Indonesian Journal of English Teaching) 11, no. 1 (July 31, 2022): 57–68. http://dx.doi.org/10.15642/ijet2.2022.11.1.57-68.
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Covid-19 pandemic has inevitably shifted the conventional face-to-face learning in higher education institution to online learning. This study examined the perceptions of Indonesian EFL students, especially freshmen, towards online learning. Employing quantitative descriptive design with 83 respondents, this study focused on 3 aspects; 1) students’ perception, 2) students’ preferences, and 3) advantage and disadvantage of online learning based on students’ experience. The result showed that based on students’ perception 1) online learning is effective (53%, n = 44) to improve English proficiency, and somewhat effective (42.2%, n = 35) to improve social competences, 2) even though students’ enjoyment shows positive trends, they still prefer face-to-face learning (62.7%, n = 52) rather than online learning (37.3%, n = 31), and 3) the most frequent choose advantage of online learning is able to stay at home (79.5%, n = 66), while the most frequent disadvantage is less interaction with lecturers and classmates (74.7%, n = 62). This research has proven that the implementation of online learning earned numerous positive perceptions, followed with several challenges that need to be overcome by any means.
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Keskin, Sinan, and Halil Yurdugül. "Factors Affecting Students’ Preferences for Online and Blended Learning: Motivational Vs. Cognitive." European Journal of Open, Distance and E-Learning 22, no. 2 (January 1, 2020): 72–86. http://dx.doi.org/10.2478/eurodl-2019-0011.
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AbstractToday’s educational institutions are expected to create learning opportunities independent of time and place, to offer easily accessible learning environments and interpersonal communication opportunities. Accordingly, higher education institutions develop strategies to meet these expectations through teaching strategies, such as e-learning, blended learning, mobile learning, etc., by using teaching technologies. These new technology-based teaching strategies are mainly shaped by decision-makers in education. This study seeks to analyse the individual factors that affect learners’ mode of teaching and learning delivery preferences. In this study, blended and online learning is considered as preferences of learners’ mode of teaching and learning delivery. The individual factors discussed in this research are cognitive learning strategies, e-learning readiness, and motivation. The data were obtained from the pre-service teachers at the end of the academic semester when they experienced online and blended learning. Data were analysed using optimal scaling analysis. The analysis method provides a two-dimensional centroid graph which shows the correlations between the variable categories. According to study findings, there is a correlation between the preferences of the learning environment, and the constructs of self-efficacy, e-learning motivation, and task value. It can be said that the motivational variables are more effective in the learning environment preference. The students with high task value, e-learning motivation, and self-efficacy preferred studying in blended learning environments. Cognitive strategies, self-directed learning, learner control, and test anxiety factors are independent of the learners’ learning delivery preferences.
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Mykhailov, N. O. "Classifications of users on online platforms using machine learning techniques." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 4 (2022): 66–71. http://dx.doi.org/10.17721/1812-5409.2022/4.8.
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Online platforms have become an integral part of our lives, and the number of users is increasing by the day. From social media platforms to e-commerce websites, these platforms are used by millions of people around the world. With such a large user base, it is essential for these platforms to classify their users based on their behavior, preferences, and interests. This paper explores how machine learning can be used to classify users on online platforms. When classifying users, they are divided into different categories based on their characteristics. By analyzing user behavior and preferences, online platforms can personalize their services and provide a better user experience. Machine learning techniques can help online platforms automate the classification process and reduce human effort. In this article, the behavioral classification of users on online platforms will be discussed in detail.
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Hosseini, Hamed Mohammad, and Sepideh Mehraein. "Learning styles and task preferences in online language courses: Match or mismatch?" Cypriot Journal of Educational Sciences 17, no. 1 (January 31, 2022): 81–94. http://dx.doi.org/10.18844/cjes.v17i1.6683.
Повний текст джерелаАнотація:
Despite the widespread recognition of learning styles (LSs) in online language learning contexts, there seems to be a paucity of research on their direct role in learners’ task preferences. Therefore, this article aims to investigate the role of LSs in learners' preferences for the specific tasks added to the typical online English learning classrooms. To accomplish this objective, data were collected through a questionnaire of LSs, task ratings and semi-structured interviews. The quantitative data revealed learners with certain dominant LSs had preferences for tasks with features consistent with their individual characteristics. The thematic data analyses went further by showing that an awareness of LSs could help learners better select their preferred tasks. It is concluded that online instructors could use tasks with specific features based on the learners’ LSs and help them have an awareness of their individual characteristics in order that they can benefit more from the instructional materials.
Keywords: Learning styles, online classroom, task design, individual differences, task features
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Santosa, Paulus Insap. "Student Satisfaction with Online Learning: A Multigroup Analysis." Register: Jurnal Ilmiah Teknologi Sistem Informasi 8, no. 2 (December 28, 2022): 122–32. http://dx.doi.org/10.26594/register.v8i2.2804.
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The Coronavirus disease 2019 pandemic “forced” students to attend online classes roughly from mid-March 2020. This situation, which caused universities, among other institutions, to deal with an overnight change in course delivery from traditional face-to-face to online mode, has resulted in many students facing difficulties. They must cope with the available infrastructure, unstable and limited Internet connection, course delivery, and their self-discipline. Male and female students may have different preferences regarding technology use. This study focused on student satisfaction with the above situation and determined whether a difference exists between male and female students using Technology Acceptance Model as the main theoretical background. Seven hypotheses were proposed and tested with the whole dataset and comparisons between the two groups. Due to the strict health protocol, an online survey was employed using Google Form to collect data. Respondents were 327 undergraduate students from one higher institution in Yogyakarta, comprising 140 male and 187 female students. The population consisted of undergraduate students who have been attending online classes since March 2022. A multigroup analysis was performed using SmartPLS 3.3.3. Results indicated no gender difference in all hypothesized relationships. The theoretical contribution can be seen from the use of Internet Quality, User Interface Quality, and Delivery Quality as the three exogenous variables of the proposed model. The practical contribution is that technology designers must pay attention to the different preferences of user groups.
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Ferdian, Nurizzi Rifqi. "ESP Students’ Preferences in Learning English: Face to Face Corrective Feedback vs Online Corrective Feedback." JETAL: Journal of English Teaching & Applied Linguistic 2, no. 1 (April 30, 2020): 29–36. http://dx.doi.org/10.36655/jetal.v2i1.199.
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ABSTRACT
The current study aims to explore the students’ preferences on face to face corrective feedback vs online corrective feedback in ESP class. It is an attempt to investigate if they prefer to be corrected directly face to face by teachers or they like to be provided online corrective feedback by the teachers. To do so, a questionnaire was distributed to a group of students who are taking English for Specific Purposes and also focus group discussion was used in order to find out their beliefs toward their corrective feedback preferences. Totally, 50 students who were taking ESP courses at State Polytechnic of Subang participated in this study. The results from the questionnaire analysis revealed that the students preferred their teachers to use face to face corrective feedback, with an overall mean (x̄ = 3,95) in learning effectiveness, (x̄ = 4,12) in learning accuracies, and (x̄ = 4,02) in learning experiences. The focus group discussion revealed the students’ beliefs in their corrective feedback preferences, they believed that the teachers should be able to guide their learning, use communicative ways, and encourage them by using their learning preferences. The study suggested that taking these preferences into consideration could help students to increase their confidence in learning English.
Keywords: Corrective Feedback, Face to face, Online, English Learning
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Gherheș, Vasile, Claudia E. Stoian, Marcela Alina Fărcașiu, and Miroslav Stanici. "E-Learning vs. Face-To-Face Learning: Analyzing Students’ Preferences and Behaviors." Sustainability 13, no. 8 (April 14, 2021): 4381. http://dx.doi.org/10.3390/su13084381.
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Educational life worldwide has been shaken by the closure of schools due to the outbreak of the coronavirus pandemic. The ripple effects have been felt in the way both teachers and students have adapted to the constraints imposed by the new online form of education. The present study focuses exclusively on the beneficiaries of the educational process and aims to find out their perceptions of face-to-face and e-learning and their desire to return, or not, to the traditional form of education. These perceptions are represented by 604 students of the Politehnica University of Timisoara, who were asked to respond anonymously to an 8-question questionnaire between December 2020 and February 2021. The results show the respondents’ levels of desire to return to school (especially of those who have only benefited from e-learning) and their degree of involvement during online classes. The results also specify the advantages and disadvantages of the two forms of education from a double perspective, namely that of first-year students (beneficiaries of e-learning exclusively), and of upper-year students (beneficiaries of both face-to-face and e-learning). The study points out key information about e-learning from the students’ perspectives, which should be considered to understand the ongoing changes of the educational process and to solve its specific problems, thus ensuring its sustainability.
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Alkooheji, Lamya, and Abdulghani Al-Hattami. "Learning Style Preferences among College Students." International Education Studies 11, no. 10 (September 27, 2018): 50. http://dx.doi.org/10.5539/ies.v11n10p50.
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The purpose of this study was to determine what factors other than individual preferences affect undergraduate students’ learning style preferences, if learning style is influenced by gender, age, college affiliation and/or type of activities. A total of 185 students from the University of Bahrain, Bahrain, participated in an online VARK (Visual, Aural, Read/Write and Kinesthetic) for younger people questionnaire. The questionnaire consisted of 16 items about learning style preferences and three about participants’ demographics. The results showed that participants generally preferred multi-modular learning style with both kinesthetic and visual learning styling being most preferred while Reading/Writing was the least preferred. Furthermore, there were statistically significant differences between students learning styles based on age and gender, but it was a moderate difference. What mostly affected the preferences, however, was the type of activities or tasks, something which in turn resulted in some difference among colleges. This suggests that VARK preferences need to be related to activity type rather than be observed at individual reference. Recommendations were provided at the end of the study.
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Wang, Victor X., and Beth Kania-Gosche. "Online Instructors." International Journal of Adult Vocational Education and Technology 2, no. 3 (July 2011): 12–29. http://dx.doi.org/10.4018/javet.2011070102.
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This study investigates the andragogical and pedagogical teaching philosophies of online instructors at the California State University, Long Beach in the Spring Semester of 2010. Drawing from reflective adult education theory, this article proposes a new model for this reflective adult education theory. It is either the helping relationship (andragogical philosophy) or the directing relationship (pedagogical philosophy) plus the learning environment (the Internet) that leads to adult learners’ critical reflection in Mezirow’s (1991) terms. A researcher-designed survey instrument called Online Philosophy of Adult Education Scale (OPAES) was used to measure instructional preferences of these instructors in the electronic classroom to determine their andragogical or pedagogical teaching philosophies. Data were collected from 37 online instructors regarding their instructional preferences. Nine qualitative questions were designed to parallel the Likert scale OPAES to determine why these online adult education instructors chose their pedagogical or andragogical teaching philosophies. The results of the study demonstrate that these online adult education instructors support both the teacher-centered approach and the student-centered approach to teaching online.
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Nguyen, Hoa-Huy, and Viet Anh Nguyen. "Personalized Learning in the Online Learning from 2011 to 2021: A Bibliometric Analysis." International Journal of Information and Education Technology 13, no. 8 (2023): 1261–72. http://dx.doi.org/10.18178/ijiet.2023.13.8.1928.
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This paper has analyzed research trends on personalized learning by bibliometric analysis method through a study of 928 articles from the Scopus database. The following issues are investigated: (1) Development scale, growth trajectory and geographical distribution of the research; (2) Outstanding authors and works on Personalized Learning; (3) Outstanding magazines and books on the topic; (4) Key themes found in these documents, and (5) Prominent methods/technologies used for personalized learning. Research results show that personalized learning is a fascinating topic in education and has been overgrown in recent years. Many researches on personalized learning comes from countries such as the United States and China. Our bibliometric analysis has revealed the main themes in the research works on Personalized Learning, such as artificial intelligence, learning style, and learning technology. The research has observed cognitive aspects of learners like knowledge level, learning style, preferences, etc. In most cases, the recommended tools and methods combined the content-based filtering, collaborative filtering, ontological approaches, etc. In addition, future research goals, difficulties, and concerns are highlighted in our work by examining the trends in several personalized learning elements.
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Chen, Junpu, and Hong Xie. "An Online Learning Approach to Sequential User-Centric Selection Problems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6231–38. http://dx.doi.org/10.1609/aaai.v36i6.20572.
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This paper proposes a new variant of multi-play MAB model, to capture important factors of the sequential user-centric selection problem arising from mobile edge computing, ridesharing applications, etc. In the proposed model, each arm is associated with discrete units of resources, each play is associate with movement costs and multiple plays can pull the same arm simultaneously. To learn the optimal action profile (an action profile prescribes the arm that each play pulls), there are two challenges: (1) the number of action profiles is large, i.e., M^K, where K and M denote the number of plays and arms respectively; (2) feedbacks on action profiles are not available, but instead feedbacks on some model parameters can be observed. To address the first challenge, we formulate a completed weighted bipartite graph to capture key factors of the offline decision problem with given model parameters. We identify the correspondence between action profiles and a special class of matchings of the graph. We also identify a dominance structure of this class of matchings. This correspondence and dominance structure enable us to design an algorithm named OffOptActPrf to locate the optimal action efficiently. To address the second challenge, we design an OnLinActPrf algorithm. We design estimators for model parameters and use these estimators to design a Quasi-UCB index for each action profile. The OnLinActPrf uses OffOptActPrf as a subroutine to select the action profile with the largest Quasi-UCB index. We conduct extensive experiments to validate the efficiency of OnLinActPrf.
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Kwiatkowska, Wioletta, and Lidia Wiśniewska-Nogaj. "Motives, benefits and difficulties in online collaborative learning versus the field of study. An empirical research project concerning Polish students." e-mentor 90, no. 3 (July 2021): 11–21. http://dx.doi.org/10.15219/em90.1518.
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As a result of universities’ growing interest in online learning, largely due to the COVID-19 pandemic, it is necessary to adapt online learning methods to students’ professional preferences. The learning environment should accordingly be designed so as to ensure the highest possible engagement levels from the participants. This article discusses the value of collaboration in online learning along with its determinants. It highlights the need to include the crucial activities in the remote education of university students while taking into account their individualization and diverse motives. The analysis reveals that students cannot be treated as a homogeneous group; the preferences and abilities represented by them – which are associated with their field of study – determine their functioning in the remote learning environment. Based on their empirical study, the authors propose recommendations that may be helpful for educators in online collaborative learning.