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Generative Artificial Intelligence (AI), especially machine learning models that autonomously generate human-like content, has recently attracted significant attention in the education sector. This paper explores the potential of generative AI, including tools like ChatGPT, to shift from traditional outcome-oriented educational practices to a more balanced approach that values both the learning process and its outcomes. Traditionally, education has emphasized achieving predefined results, but the advent of generative AI tools, which enable students to easily produce tangible results, calls for a reevaluation of these practices. This shift suggests a need for a broader focus that encompasses the entire learning process leading to the final product, thereby promoting an educational practice that equally emphasizes both the journey and the destination of learning. Recognizing that the implementation of such practices, facilitated by generative AI, still requires exploration, this paper proposes a solution that integrates the experiential learning cycle and learning portfolio. This approach is designed to demonstrate the realization of process-outcome-balanced educational practices through the use of a pedagogical AI agent.

[LANGUAGE=”English”] The COVID-19 pandemic necessitated alternative pedagogical approaches, with online autonomous learning courses emerging as a viable method for compiling learning portfolios. Consequently, online autonomous learning has garnered increasing scholarly attention. Embodying principles of openness and transcending temporal and spatial constraints, online courses afforded global learners opportunities for continued education during the pandemic. Online courses facilitate enhanced online interaction among students and teachers and allow students to control their learning experience (learner autonomy) and pace. Nevertheless, online autonomous learning presents fundamental challenges. Notably, in the absence of direct teacher and teaching assistant supervision, online autonomous learning tends to lead to lower completion rates and higher dropout rates, concerns currently under investigation by numerous researchers. In contrast to traditional teacher-centered models, online autonomous learning courses prioritize self-directed learning. Learners independently establish learning objectives and strategies commensurate with their personal learning levels to master course content. Through a series of instructional videos, in-class exercises, discussion forums, and other interactive features, an appropriate self-regulated learning mechanism was developed to guide learners toward effective autonomous learning.The exponential growth of big data in recent years has positioned artificial intelligence as a focal point of inquiry across various fields. Machine learning has catalyzed substantial advancements in the field of data science. The accumulation of extensive learning generates substantial volumes of structured and unstructured data, including the personal information of learners and various learning metrics. A growing body of research advocates for the use of data analytics as a viable method to optimize online and adaptive learning processes.Learning diagnosis entail learners’ self-assessment of requisite capabilities for learning tasks and comparative analyses of capabilities against domain expert-established concept structures by employing relevant question parameters, such as difficulty and discrimination. To facilitate this, an automated artificial intelligence material recommendation mechanism was developed, underpinned by several machine learning models. By observing online user learning behavior patterns, learning data and indicators were formulated, enabling the analysis of various online learning behaviors (e.g., watching videos and answering practice questions) and the generation of learning processes that can be viewed by learners. A practice question recommendation mechanism combined with an instant messaging application (LINE) was designed, leveraging teacher-created knowledge maps to assess students’ mastery of concepts. Zimmerman’s cyclical model of self-regulation served as the foundational framework for the recommendation mechanism.A quasiexperimental research design was employed. Participants were recruited from a calculus course taught at a university in northern Taiwan. An experimental group used reinforcement learning–recommended practice questions for self-evaluation, and a control group received randomly assigned questions. Significant improvements in scores were observed in the experimental group, and greater learning stickiness was observed compared with the control group. Consistent percentile rank increases following practice question completion suggest the system’s capacity to deliver personalized recommendations on the basis of individual differences, thereby facilitating concept-specific feedback and adaptive learning. This, in turn, fostered increased teacher–student interaction, mitigated learner isolation, and increased learning motivation, thereby strengthening self-regulated learning abilities.Upon course completion, the participants could autonomously generate artificial intelligence learning portfolios through the system on the basis of diagnostic results, creating a comprehensive record of their learning performance. These portfolios facilitated the elucidation of learner mastery levels through the accumulation of extensive learning data (big data) on the platform. A postcourse self-regulated learning questionnaire survey revealed a positive participant perception of the material recommendation mechanism and generated artificial intelligence learning portfolio. The participants demonstrated strong positive attitudes toward system reliability, learning attitudes, and metacognition but low perceptions of system utility, and low overall usage rates. Enhancing usage incentive, continuously refining the accuracy of the recommendation system’s algorithms, and conducting comparative analyses with existing systems are essential to improve the recommendation system’s perceived utility[LANGUAGE=”Chinese”] 疫情底下的線上自學課程，為學習歷程檔案提供另一種做法，但是，線上自學衍生的學習動機缺乏、無法適時提供對應的學習素材等問題，都對學習成效帶來極大的挑戰。近年來，在人工智慧發展的浪潮下，教育大數據的機器學習技術成為提升線上自學課程與個人化學習的方法之一。本研究依線上學習課程累積的大量數據，定義多向度的學習者特徵與建構機器學習模型，結合學習診斷與自我調整學習，發展一套線上自學課程的練習題推薦機制。本研究透過北部某大學所開設的微積分線上課程進行實驗，實驗組透過強化學習演算法推薦練習題，控制組則是隨機推薦練習題，結果發現實驗組的前測與後測分數達到顯著，同時實驗組學生的學習黏著度也高於控制組。整體推薦後，全部學生的PR值（百分等級）穩定上升，自我調整學習問卷則顯示學習者對於素材推薦機制與所產生的AI學習履歷持正向的感知態度。

[LANGUAGE=”English”] The historical trajectory of Taiwan’s university entrance examination system has culminated in a multifaceted platform for admission. This evolution is directly attributable to the implementation of the 2019 new curriculum guidelines of 12-year basic education. Aligned with the curriculum’s emphasis on cultivating nationally competitive talents, the traditional entrance examination has been replaced by holistic evaluation of students’ learning portfolios. Consequently, senior high school students’ learning portfolios play a crucial role in the admission process. The reformed curriculum is centered on core competencies, fostering student aptitude and adaptability through a multifaceted approach encompassing enrichment electives, diversified electives, school-developed curricula, cross-domain interdisciplinary learning, flexible learning, self-directed learning, and independent learning. This fundamental shift prioritizes individualized learning opportunities. Moreover, to stimulate student engagement, the curriculum and pedagogy emphasize rich, adaptable, diverse and cross-disciplinary learning opportunities. Considering the core competencies framework of the 2019 curriculum, college recruitment should not rely solely on advanced subject tests as selection criteria. Instead, comprehensive evaluations of the student-centered learning process are imperative. Such evaluations enable a holistic education philosophy that integrates learning and life. Talent selection by using professional competency-based rubrics necessitates the collection of diverse materials and focuses on the learning process. Accordingly, a competency-based portfolio assessment and selection methodology should be established. To this end, the talent selection mechanism for college recruitment should contextualize the student learning process, emphasize characteristic review, and facilitate the development of a robust positioning mechanism and professional assessment competencies, which are essential components of curriculum reform success.The implementation of Taiwan’s 2019 curriculum has engendered a diverse educational landscape characterized by varying school profiles and student learning trajectories and encompasses school-developed curricula, specialized electives, on-campus and off-campus engagements, flexible and self-directed learning, competitions, standardized tests, and certifications. Multifaceted experiences such as these are systematically documented in student learning portfolios and complemented by a teacher certification mechanism. To effectively evaluate these diverse portfolios, reviewers require not only specialized expertise but also refined assessment tools capable of meeting societal demands for enhanced review quality and public trust. This case study elucidated the development of competency-based rubrics through focus group interviews and empirical research and identified critical rubric components for talent selection. By understanding these criteria, students can strategically cultivate attributes valued by higher education institutions during their senior high school years.Portfolio assessment has been identified as a pivotal evaluation methodology in the Taiwanese education landscape. This study bridged the gap between university competency-based selection and senior high school talent cultivation through the lens of portfolio assessment. The study involved an examination of the perspectives and opinions of both stakeholders. By conducting a case study, focus group interviews, and a historical review of portfolio assessment rubrics, the study developed appropriate rubrics for university selection and high school talent development. Given the nascent nature of portfolio assessment tool development and calibration in the Taiwanese educational context, a case study methodology was employed to investigate the rubric creation processes of various university departments. This approach is well suited for addressing complex and underresearched problems and for exploring what to do, how to do it, and why to do it, thereby offering in-depth, multifaceted insights. Focus group interviews, a core component of the case school’s rubric development, facilitated dialogue between university and high school teachers, fostering a comprehensive understanding of portfolio assessment and culminating in the development of competency-based rubrics through collective input. Finally, this study verifies the validity of said competency-based rubrics through empirical research.The study findings reveal that competency-based rubrics developed by university departments, aligned with their educational objectives, effectively mirror the core competency emphasis of the 2019 curriculum reform. Although rarely used in college recruitment, portfolio assessment is widely used by teachers to examine student performance and assessment results. This research demonstrates the efficacy of assessing students across diverse learning domains. To address the challenge of appropriate talent selection, this study expands the traditional archival assessment approach. Focus group interviews involving university and high school faculty facilitated the refinement of competency-based rubric dimensions and promoted portfolio assessment. These interviews also fostered consensus on rubric connotations. Such dialogue between universities and senior high schools enabled universities to grasp the core competencies and multiple intelligences cultivated in high schools, whereas the senior high schools gained insights into the characteristics and skills required for university success. This study pioneers the application of focus group interviews in the development of competency-based rubrics for college admissions. Finally, the results of the correlation analysis of pre-enrollment and postenrollment student performance informed calibration of the rubric and the enhancement of its effectiveness. A dynamic correction process, incorporating a Plan-Do-Check-Action mechanism, was introduced for rubric verification and calibration. This innovative approach addresses the limitations of other evaluation rubric correction methods. The study results provide a foundation for senior high school students to cultivate university-aligned talents and offer a model for university departments to develop suitable competency-based rubrics for student selection.[LANGUAGE=”Chinese”] 大學選才制度在多次的改革下，朝向多元入學方式邁進。而隨著108課綱的推動，在這波關係到未來人才培育和國家競爭力的教育改革中，蒐集學生多元表現之學習歷程檔案逐漸成為入學審查的主要考量方式之一，其扮演極為重要的角色。有鑑於學習歷程檔案評量法（portfolio assessment）在臺灣教育情境中已日漸受到青睞並被多元運用，本研究旨在探討如何應用學習歷程檔案評量搭起建立大學選才與高中育才之間的橋梁，期藉由個案學校說明如何藉由焦點團體訪談以及實證研究等方法，瞭解大學招生專業化素養導向的招生選才觀點與看法，提出學習歷程檔案評量工具的發展歷程與校準。本研究結果發現，大學端依據學系教育目標培養之職能設計評量尺規，能呼應108課綱強調培養核心素養之精神；此外，大學端與高中端藉由焦點團體訪談的對話，有助於尺規向度描述之適當性與宣導尺規評量；最後，透過對學生在高中時期各方面表現和進入大學後表現的相關性分析結果，進行評量工具校準，以提升評量尺規之有效性。本研究之結果與意涵可供高中端作為進入大學前育才之依據，以及大學系所發展適宜選才評量工具之參考。

疫情底下的線上自學課程，為學習歷程檔案提供另一種做法，但是，線上自學衍生的學習動機缺乏、無法適時提供對應的學習素材等問題，都對學習成效帶來極大的挑戰。近年來，在人工智慧發展的浪潮下，教育大數據的機器學習技術成為提升線上自學課程與個人化學習的方法之一。本研究依線上學習課程累積的大量數據，定義多向度的學習者特徵與建構機器學習模型，結合學習診斷與自我調整學習，發展一套線上自學課程的練習題推薦機制。本研究透過北部某大學所開設的微積分線上課程進行實驗，實驗組透過強化學習演算法推薦練習題，控制組則是隨機推薦練習題，結果發現實驗組的前測與後測分數達到顯著，同時實驗組學生的學習黏著度也高於控制組。整體推薦後，全部學生的PR值（百分等級）穩定上升，自我調整學習問卷則顯示學習者對於素材推薦機制與所產生的AI學習履歷持正向的感知態度。 The COVID-19 pandemic necessitated alternative pedagogical approaches, with online autonomous learning courses emerging as a viable method for compiling learning portfolios. Consequently, online autonomous learning has garnered increasing scholarly attention. Embodying principles of openness and transcending temporal and spatial constraints, online courses afforded global learners opportunities for continued education during the pandemic. Online courses facilitate enhanced online interaction among students and teachers and allow students to control their learning experience (learner autonomy) and pace. Nevertheless, online autonomous learning presents fundamental challenges. Notably, in the absence of direct teacher and teaching assistant supervision, online autonomous learning tends to lead to lower completion rates and higher dropout rates, concerns currently under investigation by numerous researchers. In contrast to traditional teacher-centered models, online autonomous learning courses prioritize self-directed learning. Learners independently establish learning objectives and strategies commensurate with their personal learning levels to master course content. Through a series of instructional videos, in-class exercises, discussion forums, and other interactive features, an appropriate self-regulated learning mechanism was developed to guide learners toward effective autonomous learning. The exponential growth of big data in recent years has positioned artificial intelligence as a focal point of inquiry across various fields. Machine learning has catalyzed substantial advancements in the field of data science. The accumulation of extensive learning generates substantial volumes of structured and unstructured data, including the personal information of learners and various learning metrics. A growing body of research advocates for the use of data analytics as a viable method to optimize online and adaptive learning processes. Learning diagnosis entail learners’ self-assessment of requisite capabilities for learning tasks and comparative analyses of capabilities against domain expert-established concept structures by employing relevant question parameters, such as difficulty and discrimination. To facilitate this, an automated artificial intelligence material recommendation mechanism was developed, underpinned by several machine learning models. By observing online user learning behavior patterns, learning data and indicators were formulated, enabling the analysis of various online learning behaviors (e.g., watching videos and answering practice questions) and the generation of learning processes that can be viewed by learners. A practice question recommendation mechanism combined with an instant messaging application (LINE) was designed, leveraging teacher-created knowledge maps to assess students’ mastery of concepts. Zimmerman’s cyclical model of self-regulation served as the foundational framework for the recommendation mechanism. A quasiexperimental research design was employed. Participants were recruited from a calculus course taught at a university in northern Taiwan. An experimental group used reinforcement learning–recommended practice questions for self-evaluation, and a control group received randomly assigned questions. Significant improvements in scores were observed in the experimental group, and greater learning stickiness was observed compared with the control group. Consistent percentile rank increases following practice question completion suggest the system’s capacity to deliver personalized recommendations on the basis of individual differences, thereby facilitating concept-specific feedback and adaptive learning. This, in turn, fostered increased teacher–student interaction, mitigated learner isolation, and increased learning motivation, thereby strengthening self-regulated learning abilities. Upon course completion, the participants could autonomously generate artificial intelligence learning portfolios through the system on the basis of diagnostic results, creating a comprehensive record of their learning performance. These portfolios facilitated the elucidation of learner mastery levels through the accumulation of extensive learning data (big data) on the platform. A postcourse self-regulated learning questionnaire survey revealed a positive participant perception of the material recommendation mechanism and generated artificial intelligence learning portfolio. The participants demonstrated strong positive attitudes toward system reliability, learning attitudes, and metacognition but low perceptions of system utility, and low overall usage rates. Enhancing usage incentive, continuously refining the accuracy of the recommendation system’s algorithms, and conducting comparative analyses with existing systems are essential to improve the recommendation system’s perceived utility.

A context-aware ubiquitous learning environment allows applications to acquire diverse learning behaviors of u-learners. These behaviors may usefully enhance learner characteristics analysis which can be utilized to distinguish group learners for further instruction strategy design. It needs a systematical method to analyze u-learner behaviors and utilize learner characteristics for group composition. This paper proposes an effective and systematic learner grouping scheme containing transformation processes from u-portfolios to the proposed Portfolio Grid, creating a learner similarity matrix, and group composition. This study also evaluates intra-group diversity of each resultant heterogeneous group and analyzes learning behavioral patterns acquired from the study experiment. The results indicate that the proposed learner grouping algorithms had positive effects on group composition and interaction between group members for follow-up ubiquitous collaborative learning.

大學選才制度在多次的改革下，朝向多元入學方式邁進。而隨著108課綱的推動，在這波關係到未來人才培育和國家競爭力的教育改革中，蒐集學生多元表現之學習歷程檔案逐漸成為入學審查的主要考量方式之一，其扮演極為重要的角色。有鑑於學習歷程檔案評量法（portfolio assessment）在臺灣教育情境中已日漸受到青睞並被多元運用，本研究旨在探討如何應用學習歷程檔案評量搭起建立大學選才與高中育才之間的橋梁，期藉由個案學校說明如何藉由焦點團體訪談以及實證研究等方法，瞭解大學招生專業化素養導向的招生選才觀點與 看法，提出學習歷程檔案評量工具的發展歷程與校準。本研究結果發現，大學端依據學系教育目標培養之職能設計評量尺規，能呼應108課綱強調培養核心素養之精神；此外，大學端與高中端藉由焦點團體訪談的對話，有助於尺規向度描述之適當性與宣導尺規評量；最後，透過對學生在高中時期各方面表現和進入大學後表現的相關性分析結果，進行評量工具校準，以提升評量尺規之有效性。本研究之結果與意涵可供高中端作為進入大學前育才之依據，以及大學系所發展適宜選才評量工具之參考。 The historical trajectory of Taiwan’s university entrance examination system has culminated in a multifaceted platform for admission. This evolution is directly attributable to the implementation of the 2019 new curriculum guidelines of 12-year basic education. Aligned with the curriculum’s emphasis on cultivating nationally competitive talents, the traditional entrance examination has been replaced by holistic evaluation of students’ learning portfolios. Consequently, senior high school students’ learning portfolios play a crucial role in the admission process. The reformed curriculum is centered on core competencies, fostering student aptitude and adaptability through a multifaceted approach encompassing enrichment electives, diversified electives, school-developed curricula, cross-domain interdisciplinary learning, flexible learning, self-directed learning, and independent learning. This fundamental shift prioritizes individualized learning opportunities. Moreover, to stimulate student engagement, the curriculum and pedagogy emphasize rich, adaptable, diverse and cross-disciplinary learning opportunities. Considering the core competencies framework of the 2019 curriculum, college recruitment should not rely solely on advanced subject tests as selection criteria. Instead, comprehensive evaluations of the student-centered learning process are imperative. Such evaluations enable a holistic education philosophy that integrates learning and life. Talent selection by using professional competency-based rubrics necessitates the collection of diverse materials and focuses on the learning process. Accordingly, a competency-based portfolio assessment and selection methodology should be established. To this end, the talent selection mechanism for college recruitment should contextualize the student learning process, emphasize characteristic review, and facilitate the development of a robust positioning mechanism and professional assessment competencies, which are essential components of curriculum reform success. The implementation of Taiwan’s 2019 curriculum has engendered a diverse educational landscape characterized by varying school profiles and student learning trajectories and encompasses school-developed curricula, specialized electives, on-campus and off-campus engagements, flexible and self-directed learning, competitions, standardized tests, and certifications. Multifaceted experiences such as these are systematically documented in student learning portfolios and complemented by a teacher certification mechanism. To effectively evaluate these diverse portfolios, reviewers require not only specialized expertise but also refined assessment tools capable of meeting societal demands for enhanced review quality and public trust. This case study elucidated the development of competency-based rubrics through focus group interviews and empirical research and identified critical rubric components for talent selection. By understanding these criteria, students can strategically cultivate attributes valued by higher education institutions during their senior high school years. Portfolio assessment has been identified as a pivotal evaluation methodology in the Taiwanese education landscape. This study bridged the gap between university competency-based selection and senior high school talent cultivation through the lens of portfolio assessment. The study involved an examination of the perspectives and opinions of both stakeholders. By conducting a case study, focus group interviews, and a historical review of portfolio assessment rubrics, the study developed appropriate rubrics for university selection and high school talent development. Given the nascent nature of portfolio assessment tool development and calibration in the Taiwanese educational context, a case study methodology was employed to investigate the rubric creation processes of various university departments. This approach is well suited for addressing complex and underresearched problems and for exploring what to do, how to do it, and why to do it, thereby offering in-depth, multifaceted insights. Focus group interviews, a core component of the case school’s rubric development, facilitated dialogue between university and high school teachers, fostering a comprehensive understanding of portfolio assessment and culminating in the development of competency-based rubrics through collective input. Finally, this study verifies the validity of said competency-based rubrics through empirical research. The study findings reveal that competency-based rubrics developed by university departments, aligned with their educational objectives, effectively mirror the core competency emphasis of the 2019 curriculum reform. Although rarely used in college recruitment, portfolio assessment is widely used by teachers to examine student performance and assessment results. This research demonstrates the efficacy of assessing students across diverse learning domains. To address the challenge of appropriate talent selection, this study expands the traditional archival assessment approach. Focus group interviews involving university and high school faculty facilitated the refinement of competency-based rubric dimensions and promoted portfolio assessment. These interviews also fostered consensus on rubric connotations. Such dialogue between universities and senior high schools enabled universities to grasp the core competencies and multiple intelligences cultivated in high schools, whereas the senior high schools gained insights into the characteristics and skills required for university success. This study pioneers the application of focus group interviews in the development of competency-based rubrics for college admissions. Finally, the results of the correlation analysis of pre-enrollment and post enrollment student performance informed calibration of the rubric and the enhancement of its effectiveness. A dynamic correction process, incorporating a Plan-Do-Check-Action mechanism, was introduced for rubric verification and calibration. This innovative approach addresses the limitations of other evaluation rubric correction methods. The study results provide a foundation for senior high school students to cultivate university-aligned talents and offer a model for university departments to develop suitable competency-based rubrics for student selection.

學習動機是影響學習成效的重要因素，然而，高等教育所面臨的最大困境是學生缺乏學習動機。由於學生具備不同能力、興趣和動機，使得高等教育的教學場域面臨重大挑戰。本研究的動機在建立一個整合雙因子理論與遊戲化設計元素的課程設計架構，探討其如何影響不同學習動機學生在學習歷程中的參與度。研究目的在探討課程設計融入遊戲化設計元素與雙因子理論如何影響不同學習動機學生的學習參與度。本研究採混合式研究方法，透過動機問卷前測、觀察法、個案訪談及遊戲化設計後測問卷，對修課學生進行學習歷程分析。研究發現有四：一、遊戲化設計的激勵因子正向激勵內在動機高的學生。二、外在動機驅動的學習者受遊戲化設計驅動的影響較為全面，若在課程中加入遊戲化設計，會提升外在動機學生的參與度。三、保健因子中的損失趨避是遊戲化設計元素中影響無動機學習者的重要因素。四、激勵因子中的社會影響與關聯對內在動機驅動與外在動機驅動學習者皆發揮效果。據此，本研究之貢獻為提出一整合雙因子理論與遊戲化設計架構，為不同動機學習者提供課程設計 的參考。 Research Motivation and Objectives Learning motivation plays a critical role in enhancing learning effects and learning engagement. Lack of learning motivation among students is a widespread problem in higher education worldwide. The capabilities, interests, and motivations of higher education students vary, which makes it challenging to boost their learning motivation. The responsibility of higher education is not only to provide necessary knowledge and experience but also to foster students’ self-directed learning and engagement. To achieve effective engagement, a voluntary mindset must first be cultivated, which is intimately linked to motivation. Motivation is a prerequisite for successful learning. However, motivation is a psychological state, which precludes its direct assessment through observations of internal or external stimuli. Therefore, this study investigates the effect of an integrated approach involving gamification design and two-factor theory on students’ learning engagement in learning processes with different learning motivations. Theoretical Framework We propose a theoretical framework for curriculum design that integrates two-factor theory with gamification design. The proposed gamification design is based on the Octalysis framework, which involves the following eight drives of motivation (Chou, 2015; Moreira et al., 2020): epic meaning and calling, development and accomplishment, empowerment of creativity and feedback, ownership and possession, social influence and relatedness, scarcity and impatience, unpredictability and curiosity, and loss and avoidance. On the basis of two-factor theory, these eight drives were further divided into five motivators and three hygiene factors. This theoretical framework formed the foundation for the present study’s analysis. Research Method The study employed a mixed-methods research approach. Students’ learning processes were analyzed using a pretest motivation questionnaire, observations, case interviews, and a posttest gamification questionnaire. In total, 46 individuals attending a class on data analysis in a university were recruited. The study was conducted weekly throughout an 18-weeks semester. The data analysis curriculum was designed on the basis of the gamification design, with the intention of encouraging the students’ learning engagement throughout the semester. Collecting student feedback is a crucial means of analyzing learning processes. Triangulation was employed to validate the accuracy of our findings. Quantitative and qualitative data on the same phenomenon from different perspectives were collected and analyzed. Findings After triangulating data from different research methods, the following findings were obtained. First, the motivational factors of gamification design positively influenced students with high intrinsic motivation. Second, students driven by extrinsic motivation were more comprehensively influenced by gamification design. Incorporating gamification into the curriculum enhanced engagement among students with extrinsic motivation. Third, among the hygiene factors of gamification design, loss and avoidance crucially influenced learners with no motivation. Fourth, among the motivational factors of gamification design, social influence and relatedness was effective for both intrinsically and extrinsically motivated learners. Contributions This study makes some salient contributions to the literature. The study proposes a theoretical framework that incorporates the two-factor theory to classify the eight gamification drives in the Octalysis framework into five motivators and three hygiene factors. This framework was used to investigate how gamification drives affect learning engagement in the relationship between gamification design and learning motivation. Second, the findings illustrate how gamification-driven course design affects the learning engagement of students with different types of learning motivations throughout their learning processes. These results inform the design of a curriculum that fosters the engagement of students with different learning motivations. Third, this study provides insights for curriculum design in higher education by proposing different gamification-driven approaches and strategies. Research Limitations and Future Research Directions This study was conducted in the context of a data analysis course administered in a university, and the results may not be fully generalizable to courses in different disciplines. However, the triangulation of pretest questionnaires, interviews, observations, and posttest questionnaires provides insights into how gamification design elements affect students with different learning motivations. Future research could explore and compare these effects across different disciplines in higher education.

Background: Learning portfolios (LPs) provide evidence of workplace-based assessments (WPBAs) in clinical settings. The educational impact of LPs has been explored in high-income countries, but the use of portfolios and the types of assessments used for and of learning have not been adequately researched in sub-Saharan Africa. This study investigated the evidence of learning in registrars’ LPs and the influence of the training district and year of training on assessments.Methods: A cross-sectional study evaluated 18 Family Medicine registrars’ portfolios from study years 1–3 across five decentralised training sites affiliated with the University of the Witwatersrand. Descriptive statistics were calculated for the portfolio and quarterly assessment (QA) scores and self-reported clinical skills competence levels. The competence levels obtained from the portfolios and university records served as proxy measures for registrars’ knowledge and skills.Results: The total LP median scores ranged from 59.9 to 81.0, and QAs median scores from 61.4 to 67.3 across training years. The total LP median scores ranged from 62.1 to 83.5 and 62.0 to 67.5, respectively in QAs across training districts. Registrars’ competence levels across skill sets did not meet the required standards. Higher skills competence levels were reported in the women’s health, child health, emergency care, clinical administration and teaching and learning domains.Conclusion: The training district and training year influence workplace-based assessment (WPBA) effectiveness. Ongoing faculty development and registrar support are essential for WPBA.Contribution: This study contributes to the ongoing discussion of how to utilise WPBA in resource-constrained sub-Saharan settings.

Purpose The purpose of this paper is to explore opportunities for delivering sustainable leadership education through critical reflection embedded in the framework of higher and degree apprenticeships. Design/methodology/approach This paper contributes to leadership development research that focusses on “leader becoming” as an ongoing process of situated learning (in the classroom and everyday work life). The approach to leadership development adopted in this paper proposes that sustainable leadership practices and decision making are developed when leadership learning is firmly embedded in work-based practices and critical self-reflection. Findings The discussion of critical reflection methods focusses on utilising the learning portfolio as a core aspect of all leadership and management apprenticeships to embed sustainable and reflective practice and facilitate situated leadership learning. The paper explores the role of training providers in actively connecting higher and degree apprenticeships to embed this model of leadership development and seeing leadership as a lifelong apprenticeship. It also highlights the potential for resistance by managers and senior leaders in seeing themselves as apprentices rather than accomplished leaders. By paying attention to issues of language and identity in this discussion, it will surface practical implications for the delivery of sustainable leadership education through the framework of apprenticeships. Originality/value This paper adds to the theoretical and practical understanding of sustainable leadership education by exploring opportunities for re-framing leadership development as a lifelong apprenticeship focussed on personal and professional development. Recognising the resistance that often exists to reflective practice within leadership development contexts, this paper further explores ways of dealing with such resistance.

Background: With the rapid increase in the aging population, a greater number of older individuals will require nursing care in the future. Therefore, it is important for nurses to be willing to engage in gerontological nursing. Nursing students must increase their experience in providing care to older people during their education and must receive education that improves their attitudes toward aging; this will help provide care to the older people, develop positive attitudes toward aging, and increase their empathy and willingness to provide care to older people after graduation. Hence, studies focused on improving the attitude of nursing students toward aging are urgently required. Methods: In this mixed-method experimental study, participants were interviewed individually and observed to better understand the connection between quantitative and qualitative data. Service learning and learning portfolio constructions were integrated in the gerontological nursing curriculum of an experimental group, whereas traditional gerontological nursing curriculum was provided to a control group. Quantitative data on the nursing students were collected using the attitudes toward aging scale (ATAS) and older people behavioral intention scale (OBIS) and analyzed using descriptive and inferential statistics. Result: From the pre- to the post-test, the average ATAS and OBIS scores of the experimental group increased significantly, reaching a statistically significant level. However, the results of the control group indicated that the educational intervention does affect the attitudes toward aging and older people behaviors. A qualitative analysis revealed that educational intervention can improve the students’ attitudes toward aging and older people behavioral intention. Conclusion: Our study results showed that integrating community older people service and learning portfolio construction into the curriculum can effectively improve the attitudes of nursing students toward aging and older people’s behaviors, thus providing substantial assistance to students intending to care for the older people in the future.