Approaches to identifying personalised learning needs: an analysis of evidence

Personalized learning has become an important and powerful paradigm catering for various needs, styles, preferences, and modes of learning. Several methods including task recommendations and path planning have recently emerged to effectively implement personalized learning using e-learning systems. The literature shows that the use of task recommendations in e-learning systems is a very effective way to facilitate personalized vocabulary learning. One of the key research issues regarding these personalized vocabulary learning systems is how to model the learning logs of each learner. Specifically, how to measure the learning effectiveness of each learned tasks has become a core issue for establishing personalized learning systems. Three theories, Spaced Learning (SL), Technique Feature Analysis (TFA), and Involvement Load Hypothesis (ILH), are commonly applied for achieving this purpose. In this study, we compared the effectiveness of these three linguistic theories for modeling EFL learners’ personalized vocabulary learning via task recommendations. By conducting experimental studies among different groups of participants, our findings revealed that the ILH and TFA were more suitable than SL for facilitating personalized vocabulary learning. It is therefore suggested that future personalized vocabulary learning systems ought to be designed and developed based on comprehensive theoretical frameworks such as the ILH and TFA.

Since 2000, education reform has quietly risen. As one of the research hotspots of education reform, individualized learning has been valued by various countries. The emergence of the online learning space provides a huge technical environment support for personalized learning. However, how to recommend personalized learning content for learners from the numerous resources on the Internet has always been one of the difficulties in personalized online learning. Technology is a key element for achieving personalized online learning. At present, there are fewer systematic researches and practical applications that combine personalized learning with online learning space. In order to better realize the personalized learning of students and make the personalized learning develop to a higher level, this paper studies the personalized recommendation. The research of personalized recommendation system is an important breakthrough in the realization of information filtering. It is usually based on the user's historical interaction information to mine potential interests and preferences to push items that satisfy users. In this paper, the knowledge representation learning method is used to retain the semantic information of the user or the item itself, and the data is embedded in the low-dimensional vector space to calculate the semantic similarity to generate the recommendation result. This algorithm enriches the semantic information of users or items in traditional recommendation algorithms, effectively enhances the recommendation performance, and makes better recommendations for network personalized learning.

Data mining for providing a personalized learning path in creativity: An application of decision trees

The paper presents the analysis of strategies and challenges of technology-enhanced personalized language learning at technical universities. The authors specify the meaning of the “personalization” concept in terms of language learning and characteristics of technologyenhanced learning. Skills of personalized learning contribute to the development of learner autonomy and equip students with flexibility and adaptability to the changing demands of today’s labor market. The empirical study was carried out among 101 students from two universities. The questionnaire was aimed at measuring the level of personalized learning skills and digital resources that promote the personalized learning. Students were suggested three questionnaires: one survey on personalized learning skills was performed before and after the experiment, reflection questionnaire and on digital resources ranking were suggested after the experiment. After the introduction of technology-enhanced personalized learning strategies into the educational process, the level of personalized learning skills increased as well as students’ perception of this educational phenomena. There was no significant difference between the results of students from different universities in terms of personalized learning skills. It means that the problem of personalized learning is relevant to the majority of students and requires solutions. The paper provides examples of digital resources’ practical application that intensify the personalization with a promising transition to selfimprovement and successful professional self-realization.

Students often have difficulties in self-regulating their vocabulary learning in mobile-assisted language learning (MALL). Building on past studies of vocabulary learning, MALL, self-regulation, and personalised learning (PL), we propose a self-regulated, collaborative, personalised vocabulary (SCPV) learning approach in MALL. In this exploratory mixed-methods study, 35 university students learned second language (L2) vocabulary via the SCPV or a self-regulation-only (S) approach. Data were collected through pre- and post-surveys, personalised vocabulary tests, and interviews. The results indicated that the new approach may hold more potential to help learners achieve better productive vocabulary knowledge. Thematic analyses of interviews indicated that the SCPV students enhanced their vocabulary learning; specifically, these students demonstrated a systematic understanding of vocabulary learning processes. Furthermore, specific PL roles (e.g., community sharing of self-regulated vocabulary learning) showed how collaborative PL could aid participants' development of self-regulated learning. Implications include how to conduct self-regulated training in MALL and designing both individual and collaborative tasks that involve PL.

A Design Framework for Personal Learning Environments

This study examined the roles of 3 multilevel motivational predictors in protégés' personal learning in teams: an autonomy-supportive team climate, mentors' autonomy support, and protégés' autonomy orientation. The authors followed 305 protégés in 58 teams for 12 weeks and found that all 3 predictors were positively related to the protégés' personal learning in teams and that an autonomy-supportive team climate augmented the effects of mentors' autonomy support and protégés' autonomy orientation on protégés' personal learning in teams. Protégés' personal learning in teams mediated the interactive effects of an autonomy-supportive team climate with mentors' autonomy support or protégés' autonomy orientation on protégés' behavioral and attitudinal outcomes, including their organizational citizenship behaviors and job involvement. The findings of this study provide business researchers and practitioners with valuable insights into the management of autonomy.

The pace of organizational and environmental change seems to demand that such professional organizations as CPA firms become learning organizations in order to compete with other firms. The flattening or thinning out of traditional hierarchical structures within organizations argues that traditional mentoring and supervisory structures may be inadequate for fostering needed individual learning about (a) organizational goals and politics, and (b) personal learning about job context and skill development, thus the ability of individuals within CPA firms to master new skills may be impaired. One effect of the lack of such learning may be increased role stress, job burnout, loss of commitment to the organization, intention to leave, and diminished job satisfaction. Using a sample of 440 accounting professionals from major CPA firms in the southeastern, southwestern, and northeastern regions of the United States, this research studies the ability of peer developmental relationship functions foster the requisite personal and organizational learning, and also to directly and indirectly influence attitudinal outcomes. We further examine whether the impact of developmental peer relationships on attitudinal outcomes is mediated by personal and organizational learning.A hierarchical regression-based test was used to evaluate our hypotheses. The results partially supported our expectations that such peer developmental relationship functions were significantly related to attitudinal outcomes, to elements of organizational learning, and to the skill development aspect of personal learning. Elements of organizational socialization, personal learning, and team-source learning were significantly related to attitudinal outcomes as well.

With the popularization of the Internet, the rapid development of intelligent education is promoted. They are a new platform for students to learn, and they can quickly help students improve and consolidate knowledge, as well as unlimited time, place, and space. However, due to a large number of learning resources, how to find the resources you need, and suitable from the massive resources is a problem that needs to be solved in this field. The individual characteristics of each learner are different, so when meeting their own needs, they have different requirements for individualization. The personalized learning resource recommendation system was born, according to the different characteristics of students to recommend corresponding learning resources. Since most of the recommendation systems currently recommend a large number of test questions or a large number of books, but each person’s needs are different, this article focuses on the above problems and selects a small piece of content course knowledge points in the learning resources, using course knowledge Come as a recommendation point and solve the above problems by designing a personal personalized learning mechanism. The main research contents of this article are as follows: (1) It is proposed to take curriculum knowledge points as the recommended objects. (2) Use collaborative filtering methods and cognitive diagnosis methods to design individual learning mechanisms. (3) Experiment. Through related experiments, the use of collaborative filtering method and cognitive diagnosis method based on curriculum knowledge points recommendation to confirm whether it is feasible.

The integration of intelligent technologies in music education heralds a new era, facilitating personalized learning experiences. This paper explores innovative approaches to enhance music education through algorithmic solutions. The aim of the study is to develop an intelligent Music Education System (MES) that harnesses algorithms to create personalized learning paths for students. A dataset comprising students’ online music learning behaviors, capturing various aspects of their interaction with music education platforms is utilized. Data pre-processing involves standardization and normalization techniques to ensure consistency. The Intelligent Generative Adversarial Network (IGAN) is employed for the automatic generation of music accompaniment, while a Redefined Adam Optimization (RAO) algorithm is utilized to optimize the recommendation process for personalized learning paths. This system incorporates intelligent algorithms to provide adaptive learning experiences tailored to individual student needs, fostering improved engagement and outcomes. Preliminary results indicate significant enhancements in student performance with a marked increase in final course grades compared to traditional methods. The proposed RAO-GAN strategy fared better than the conventional approaches, with student satisfaction ratings of 94.5%, skill advancement scores of 94%, and emotional expression ability scores of 93%. In personalized music learning, the proposed RAO-IGAN strategy showed remarkably consistent performance across the main parameters, with precision, [Formula: see text]1-Score, recall and accuracy of 0.99, 0.98, 0.98 and 0.99. The established intelligent MES successfully merges music education with advanced algorithms, offering a framework for cultivating innovative musical talents through personalized learning experiences.

The majority of large, complex projects suffer disruptions. These can have unexpected and significant impacts on a project, resulting in excessive time and cost overruns. Disrupted projects therefore require careful management in order to minimize the impact of a disruption. One element of a project for which disruptions will have a particular impact is on any anticipated learning gains. Loss of learning from disruptions can often be very significant. This lost learning may be from individual workers (personal) or by the organization (corporate). An understanding of the impact of a particular disruption on learning is required to enable effective management of that disruption. This paper argues that improved management of learning in disrupted projects may come from the disaggregation of personal and corporate learning from the typically used aggregated learning curve presumptions. The literature is reviewed to explore whether a method of disaggregation can be determined from existing propositions about the behaviour of learning curves. The explorations demonstrate that the disaggregation of a learning curve is complex. The contribution of this paper derives from developing an understanding of the role of asymptotes in constructing learning curves, and the nature of the interaction between personal and corporate learning. These two aspects are argued to be crucial aspects in managing the impact of disruptions on learning. The main motivation for this work is to provide managers with an understanding of the disaggregation process, in order that they can think through the impact of a disruption on learning in their own projects.

Despite the turbulent economy, recent expenditures on workplace learning in North America have increased. Technology-based methods including tools that enable social learning are making significant gains and account for 39% of all training hours in 2012. A majority of companies are moving from static classroom training to workplace learning that is more interactive and driven by technology. Companies actively experiment with new methods such as personalized learning, performance support, and gamification to encourage employees’ motivation to learn and promote continuous workplace learning, practice and application. However, the divide between the training and competencies people have and the training and competencies companies need still remains. The National Research Council Canada (NRC)’s Learning and Performance Support Systems (LPSS) program, by implementing adaptive and personalization strategies, develops software components for learning, training, performance support and enterprise workforce optimization. These technologies have the potential to facilitate lifelong learning, reduce learning and training costs, and reduce demands on physical infrastructure. Software components being developed for learning, training and performance support also enable streamlined and rapid skill development, as well as reduce time to competency, support informal, personal and personalized learning, increase learner engagement, address workforce optimization and sustainability, and increase operational performance and productivity. An overview of the LPSS system and capabilities is presented along with the results of our review of the current state of competency management in Canada and some challenges in this area, followed by recommendations for further work on competency functionality in the context of the LPSS program.

The Report is prepared on the basis of the information received from three European universities – the Project’s partners (including information from the universities’ websites) about the practices of personalised learning (PL); and the information from six Ukrainian universities – the Project’s partners (including surveys of staff and students) to describe the current state/situation, to understand the existing gaps and to define the tasks for the process of implementing the Project. Information about the European universities presents their achievements in the realisation of PL in higher education, including the following directions: PL model; Institutional policies and PL; Infrastructure, environment, tools etc. for PL; QA system and PL; Students and PL; University teachers and PL; Management and PL; Inclusion in the education process. Information on Ukrainian universities has two parts: the 1st – General questions about implementing PL in the university; the 2nd – Questionnaire for surveying staff and students. The Conclusions that are finalising the publication have a practical orientation.

This study examines the essential role of educators as facilitators of personalized learning environments in moroccan schools. As education systems around the world increasingly recognize the importance of personalized learning to meet the various needs of students, understanding the specific role of teachers is becoming paramount. This research examines the practices, challenges and potential opportunities faced by educators in implementing personalized learning approaches in moroccan classrooms. The study uses a mixed-method approach, combining qualitative and quantitative data collection methods. Qualitative data is obtained through in-depth interviews with teachers exploring their views on personalized learning and the associated roles and responsibilities of educators. In addition, a survey questionnaire is distributed to teachers to collect quantitative data, which allows a broader understanding of the prevalence and effectiveness of personalized learning practices in different school contexts. The data collected will be analyzed using thematic analysis and statistical techniques to identify emerging themes, models and correlations. The results of this study will contribute to all existing knowledge on personalized learning and highlight the specific challenges and opportunities faced by educators in moroccan schools. By exploring the role of teachers as facilitators in personalized learning environments, this research will inform education decision-makers, administrators and teachers themselves on effective strategies to promote personalized learning approaches in moroccan classrooms. The expected implications of this research include the identification of best practices and strategies to integrate personalized learning into the moroccan education system. The results of the study could lead to the development of teacher training programs, professional development initiatives and policy recommendations that meet the needs of educators and students in personalized learning environments. Finally, this research aims to support the improvement of educational practices in moroccan schools, by promoting a student-centered approach that promotes personalized learning experiences.

BackgroundPersonalised learning, an educational approach that tailors teaching and learning to individual needs and preferences, has gained attention in recent years, particularly in higher education. Advances in educational technology have facilitated the implementation of personalised learning in various contexts. Despite its potential benefits, the literature on personalised learning in health sciences higher education remains scattered and heterogeneous. This scoping review aims to identify and map the current literature on personalised learning in health sciences higher education and its definition, implementation strategies, benefits, and limitations.MethodsA comprehensive search of electronic databases, PubMed, Scopus, Google Scholar, Educational Research Complete, and Journal Storage (JSTOR), will be conducted to identify relevant articles. The search will be limited to articles published in the English language between 2000 and 2023. The search strategy will be designed and adapted for each database using a combination of keywords and subject headings related to personalised learning and health sciences higher education. Eligibility criteria will be applied to screen and select articles. Data extraction and quality assessment will be performed, and thematic synthesis will be used to analyse the extracted data.DiscussionThe results of the scoping review will present a comprehensive and coherent overview of the literature on personalised learning in health sciences higher education. Key themes and topics related to personalised learning, its definitions, models, implementation strategies, benefits, and limitations, will be identified. The geographical and temporal distribution of research on personalised learning in health sciences higher education will also be described. This scoping review will provide a structured synthesis of the available evidence on personalised learning in health sciences higher education, highlighting potential gaps and areas for future research. The findings will contribute to ongoing scholarly and policy debates on personalised learning in higher education, informing the development of best practices, guidelines, and future research agendas.