A formal model for assessing the learning outcomes of academic programs.

The SOLO Taxonomy is one of the effective tools for determining students’ levels of learning. Within the scope of this research, the learning and sub-learning outcomes associated with domain-specific skills in the Century of Türkiye Education Model Preschool Education Program were examined according to the levels of the SOLO Taxonomy. The research process was carried out within the theoretical framework of document analysis, a qualitative research method. The Century of Türkiye Education Model Preschool Education Program comprises seven main learning area skills. In this context, considering their operational characteristics, 98 learning outcomes and 329 sub-learning outcomes were analyzed according to SOLO Taxonomy levels. The findings of the research indicate that, across all learning area skills and age groups within the Program, the majority of both learning outcomes and sub-learning outcomes correspond to the “relational” level. While the “unistructural” level was least represented among the learning outcomes, the “extended abstract” level was the least represented among the sub-learning outcomes. The research aimed to highlight the importance of incorporating the fundamental principles of the SOLO Taxonomy into the interpretation of learning outcomes related to learning area skills at the preschool education level, as well as into the evaluation of children’s achievements within the context of the Century of Türkiye Education Model Preschool Education Program. In light of the research findings, it is recommended that the learning experiences of both young children and teachers in early childhood education be enhanced in accordance with the principles of learning depth articulated by the SOLO Taxonomy. Accordingly, the development of rigorous instructional models and evidence-based educational materials is advised, alongside the strengthening of curriculum learning outcomes to ensure alignment with higher-order cognitive structures.

This study investigates the practicality of an inquiry-based laboratory module on atomic structure and nanomaterials to enhance vocational high school students’ relational understanding, as outlined in the SOLO Taxonomy. The method employed is Research and Development (R&D) using the ADDIE model, which involves needs analysis, design, development, limited trials, and module evaluation by students and teachers of Agrobusiness and Horticulture at SMK Negeri 2 Banjit. Practicality data were obtained through a Likert-scale questionnaire assessing ease of use, student engagement, alignment with the SOLO Taxonomy, as well as the evaluation and impact of learning. The practicality test results show that all aspects of the module were rated as very practical, with average scores of 98.0% from teachers and 89.13% from students, indicating the module’s strong feasibility for classroom implementation in vocational chemistry learning. The integration of inquiry-based learning with the SOLO Taxonomy demonstrates meaningful pedagogical value in supporting students’ progression toward relational-level conceptual understanding and strengthening active engagement with abstract chemical concepts such as nanomaterials. Scientifically, this research contributes an empirically validated instructional model that bridges theory and laboratory practice, offering a scalable approach to enhancing higher-order thinking skills and practical competence in vocational science education. The findings underscore the importance of structured inquiry modules as a strategic approach to enhance the relevance and effectiveness of chemistry learning, thereby preparing students for industrial and professional demands.

This paper examines how humanised AI avatars powered by large language models are reshaping online learning and asks whether they can act as genuine participants in educational dialogue rather than mere imitators of human conversation. It situates this question in the wider context of recent demonstrations that conversational AI can pass behavioural versions of the Turing Test, arguing that the key issue for educators is not imitation but authenticity within dialogic spaces. Drawing on a multi-iteration design-based research study in an online Executive MBA programme, the study combines dialogic theory with quantitative and qualitative analysis, using Tech-SEDA coding, the SOLO taxonomy and a Naïve Bayes classifier to link features of asynchronous discussion to levels of cognitive engagement and summative performance. The findings show that AI-mediated interventions can significantly increase higher-order dialogic moves, deepen students’ conceptual understanding and predict learning outcomes from dialogic quality with high accuracy, while also highlighting important limitations around authenticity, agency and the absence of machine consciousness. These results imply that humanised AI avatars can be productively used as scalable simulators and mediators of dialogue when they are transparently framed as non-conscious tools, carefully orchestrated by teachers, and embedded within ethical design principles that protect student autonomy, promote critical AI literacy, and cultivate genuinely dialogic, polyphonic learning communities.

This study investigates the integration of a smartphone-based virtual laboratory into a fourth-semester undergraduate fluid mechanics class on pump–piping systems. The virtual laboratory is designed according to constructive alignment and the SOLO taxonomy to foster deep learning. Students interact with realistic 3D system models, adjust component parameters, and receive real-time feedback based on physical simulations. To identify the effectiveness, a pre- and post-test with 26 paired responses showed a small overall improvement in general knowledge, with medium-to-large gains in specific methodological knowledge and selfassessed competence in handling real fluid systems. Student feedback was collected to assess the appeal of the teaching method. Students rate it highly positive (mean rating = 4.42/5), highlighting increased motivation, engagement, and active participation compared to conventional teaching. Future work will expand the app with additional levels targeting diverse learning objectives in fluid mechanics.

This mentorship program addressed gaps in classroom design and formative assessment at SDN 24 Dauh Puri notably, limited capacity to assess depth of student understanding and insufficient application of deep-learning strategies. This program combined in-service and on-service model to integrate theoretical grounding with classroom practice. Activities comprised an introductory session on SOLO Taxonomy and Tri Pramana, collaborative lesson-plan workshops, followed by structured independent classroom trials by 16 teachers,, and an online reflection and evaluation meeting. Evaluation drew on lesson-plan artifacts, teachers’ implementation reports, formative assessment observations, and synthesized teacher testimonials. Results indicate full conceptual uptake among participants (mean understanding 100%), successful production of SOLO-aligned Lesson Plans, and at least two implementations per teacher. Observable impacts included more systematic student reasoning (movement toward relational and extended-abstract levels), enhanced reflective classroom interactions via the Tri Pramana cycle, and improved formative assessment precision through SOLO-based rubrics. The study recommends creating a rubric/item bank, extending practical implementation time, and establishing follow-up collaborative forums to sustain and scale the instructional innovation.

Education in the 21st century requires students to have high-level thinking skills (HOTS), but the low HOTS of students' mathematics is still a significant challenge, as reflected in the results of international evaluations. This study aims to investigate the effectiveness of the integration of deep learning and SOLO Taxonomy in optimizing students' mathematics HOTS. Using a quasi-experimental design, the study involved 60 of students in grade VIII who were divided into an experimental group (n = 30) and a control group (n = 30). The experimental group received a learning intervention based on deep learning principles with the guidance of the SOLO Taxonomy, while the control group received conventional learning. Data were collected through a math HOTS pre-test and post-test and analyzed using ANCOVA to control students' initial scores. The results showed a significant increase in students' math HOTS in both groups, but the increase in the experimental group was much higher (p<0.01). The qualitative analysis of the students' responses also shows a clear development at the level of SOLO Taxonomy, from Unistructural/Multistructural to Relational and Extended Abstract. These findings indicate that the integration of deep learning and SOLO Taxonomy creates an effective synergistic effect in facilitating deep thinking. This research contributes to the educational literature by providing a measurable and practical learning model to improve HOTS, as well as providing important implications for teacher practice and curriculum development in the future.

This study aims to describe the application of SOLO Taxonomy-based early childhood development assessment instruments at Al-Huda Kindergarten in Gorontalo City. The research approach uses a descriptive qualitative method with data collection techniques in the form of observation, interviews, and documentation. The focus of the research includes teachers' understanding of the assessment instruments, the filling process, and the suitability of the instrument application with the principles of SOLO Taxonomy. The results show that the school has implemented four assessment instruments, namely anecdotal notes, checklists, work results, and serial photos as part of formative assessment. In general, the assessment implementation was in line with the children's development, but there was a discrepancy in the use of anecdotal notes, which should have been written one sheet per child but were used for several children at once, thereby reducing the accuracy of the children's development data. The SOLO Taxonomy helped teachers classify the level of complexity of children's understanding from pre-structural to extended abstract, making the assessment more meaningful. This study emphasizes the importance of applying appropriate and individualized assessment instruments so that information on child development is more accurate and can be used to design appropriate follow-up learning activities.

Deep learning has become a central theme in contemporary educational reform, representing a critical indicator of learning quality. Peer feedback, as an interactive and learner-centered approach, has been shown to foster students' cognitive and meta-cognitive growth and holds significant potential for facilitating deep learning. This study constructed a peer assessment framework to promote deep learning in blended teaching and designed corresponding activities and implementation procedures. Drawing on CIMO-logic, the study examined how peer assessment triggered mechanisms such as personal engagement, seeking and providing relevant feedback, iterative exploration, and understanding one's own learning process. Data were collected through the SOLO taxonomy, rubrics, and questionnaires, complemented by discourse analysis of peer feedback comments. The linguistic analysis revealed that metalinguistic explanations and elicitation questions were associated with cognitive and ability development, while praise and politeness strategies primarily supported emotional engagement. The findings provide empirical evidence that peer assessment promotes deep learning across cognitive, ability, and emotional dimensions, and demonstrate that linguistic strategies in feedback are integral to how students process and internalize learning. This study provides theoretical insights into the occurrence of deep learning and offers practical implications for designing peer feedback activities to enhance learning quality in blended educational settings.

BackgroundThis article provides an analysis of the pedagogical underpinnings that characterise the research integrity (RI) training approaches and materials developed by four European Union - funded projects. The approaches taken by these projects include a dialogical approach, an empowerment approach, a virtue ethics approach, and a constructivist case-based approach. We analysed the materials/trainings created in the projects. In doing so, we propose how to make use of a structured way of working with learning objectives, in order to ensure that research integrity training meets the needs of its target groups.MethodsWe applied qualitative, deductive content analysis in which we analysed the learning objectives stated in the training schemes of the four projects. A total of 46 learning objectives were analysed using three learning taxonomies, namely the revised Bloom's Taxonomy, the SOLO Taxonomy, and the Taxonomy of Significant Learning.ResultsThe results show that the four RI trainings make use of either a constructivist or a socio-constructivist understanding of learning, implement activating and engaging learning activities, and emphasise high-order learning objectives.ConclusionsThe analysis suggests that taxonomies are applicable to various pedagogical underpinnings and can help trainers to communicate the objectives of training and learners to relate their expectations to the objectives. We identify implications for training design and suggest recommendations for training developers. It is advisable to pay attention to learning objectives dedicated to foster the higher levels of learning and understanding. Less commonly applied taxonomies in the context of integrity training, such as the Taxonomy of Significant Learning can help to identify relevant learning objectives both for trainers as well as learners. Research cultures and disciplinary differences are generally not spelled out at the level of learning objectives highlighting the need to consider these explicitly in training implementation.

ABSTRACT The rise of generative artificial intelligence (GAI) offers transformative opportunities and challenges for higher education, prompting a critical question: How can this technology reshape learning while preserving the unique strengths of human cognition? This paper explores how GAI can be integrated into a heutagogical framework to enhance teaching, learning, and assessment, fostering human intelligence and agency. Heutagogy, derived from the Greek word ‘heutos’ meaning self, is a pedagogical approach that emphasizes self-determined learning, where learners take significant control over their educational journeys, including the design of their curriculum and assessment processes. By analyzing AI's strengths and limitations, the paper highlights how AI's computational capabilities can complement human cognition's emotional and ethical dimensions. A novel AI-augmented heutagogical framework is proposed, aligning with increasing learner autonomy and cognitive complexity as defined by the SOLO Taxonomy. This framework enables personalized learning pathways while fostering both crystallized and fluid intelligence. Successful integration requires intentional pedagogical shifts, faculty development, and strategic infrastructure investments. By emphasizing learner agency and self-directed learning, heutagogy supports intrinsic motivation and critical twenty-first-century competencies. This approach positions higher education to evolve beyond merely building human capital toward fostering comprehensive human development in an AI-augmented world.

This article provides an analysis of the pedagogical underpinnings that characterise the research integrity (RI) training approaches and materials developed by four European Union - funded projects. The approaches taken by these projects include a dialogical approach, an empowerment approach, a virtue ethics approach, and a constructivist case-based approach. We analysed the materials/trainings created in the projects. In doing so, we propose how to make use of a structured way of working with learning objectives, in order to ensure that research integrity training meets the needs of its target groups. We applied qualitative, deductive content analysis in which we analysed the learning objectives stated in the training schemes of the four projects. A total of 46 learning objectives were analysed using three learning taxonomies, namely the revised Bloom's Taxonomy, the SOLO Taxonomy, and the Taxonomy of Significant Learning. The results show that the four RI trainings make use of either a constructivist or a socio-constructivist understanding of learning, implement activating and engaging learning activities, and emphasise high-order learning objectives. The analysis suggests that taxonomies are applicable to various pedagogical underpinnings and can help trainers to communicate the objectives of training and learners to relate their expectations to the objectives. We identify implications for training design and suggest recommendations for training developers. It is advisable to pay attention to learning objectives dedicated to foster the higher levels of learning and understanding. Less commonly applied taxonomies in the context of integrity training, such as the Taxonomy of Significant Learning can help to identify relevant learning objectives both for trainers as well as learners. Research cultures and disciplinary differences are generally not spelled out at the level of learning objectives highlighting the need to consider these explicitly in training implementation.

ABSTRACT In response to 21st-century demands for students’ Quantitative Reasoning Skills (QRS), the unique quantitative reasoning characteristics of the lower-secondary school chemistry, and the need for QRS assessment in the discipline, this study developed an instrument to assess Students’ Chemistry Quantitative Reasoning Skills (QRSC). The QRSC assessment, designed based on the SOLO taxonomy, curriculum standards, and the Higher Secondary School Entrance Examination (HSSEE) criteria, comprised 26 items and was administered to 237 students. After using Rasch analysis to score students’ performances, the study conducted descriptive statistics, independent samples t-tests, ANOVA, and the general linear model to analyse students’ overall QRSC, gender and academic achievements differences in QRSC, and the interactive effect between gender and academic achievement on QRSC. The results showed that the students’ overall QRSC level was relatively good and widely distributed. Gender had no significant effect on lower-secondary school students’ QRSC; however, there was a significant difference in QRSC among students with different academic achievements. In addition, the study found a significant interaction between gender and academic achievement in QRSC. Specifically, academic achievement can mediate the influence of gender on QRSC. Based on these findings, the study made some pedagogical recommendations for students’ QRSC development.

This research aims to analyze critical thinking skills based on the SOLO Taxonomy in junior high school students with high, medium, and low learning independence categories. The type of research used is qualitative descriptive. The subjects of this study are eighth-grade students of SMP Negeri 1 Maduran. The data collection techniques used are questionnaires, tests, and interviews. Data analysis is conducted qualitatively through the stages of data reduction, data presentation, and drawing conclusions. The research results conclude that students with high learning independence are able to meet all indicators of critical thinking skills and all 3 levels of the SOLO taxonomy. Students with moderate learning independence are able to meet 4 indicators of critical thinking skills and 2 levels of the SOLO taxonomy. Students with low learning independence can only meet 2 indicators of critical thinking skills and 1 level of the SOLO taxonomy.

Although the Ghanaian mathematics curriculum emphasizes critical thinking as a core competence, students still appear to lack this skill. In this mixed-method study, the Structure of the Observed Learning Outcome (SOLO) taxonomy was used to assess senior high school students’ thinking levels in permutation and combination. A sample of 256 males and 104 females was randomly selected from three senior high schools for the study. The data were collected using tests and interviews, and analyzed descriptively and inferentially using Kruskal-Wallis tests. The results showed that while only one-fifth of the students reached the higher relational and extended abstract thinking levels, the majority (73.9%) remained at the lower levels of pre-structural, uni-structural, and multi-structural thinking. These students struggled to apply basic counting and multiplication principles in solving higher-order thinking problems. The Kruskal-Wallis H test further revealed statistically significant differences in thinking levels across the study programmes. General Science students demonstrated the highest thinking levels, followed by General Agriculture and Business students. The study concluded that students’ thinking levels in permutation and combination were low. It is recommended that teachers, textbook authors, and curriculum developers adopt representations and activity-based teaching strategies to help students develop a conceptual understanding of the topic.

This research is motivated by the dominance of quantitative learning evaluations and the element of subjectivity in qualitative learning evaluations, as there is no established standard in the field of science for grades VI-B at SD Negeri Bojonglongok. The purpose of this study is to describe the improvement of science learning outcomes from the perspective of the "structure of observed learning outcome taxonomy" in grade VI-B students at SD Negeri Bojonglongok through the application of a deep learning approach using the station learning model. This study is a Classroom Action Research using the Kemis and Taggart model. The results showed an increase in science learning outcomes from the perspective of the observed learning outcome taxonomy structure in grade VI-B students at SD Negeri Bojonglongok, achieved through the application of a deep learning approach using the station learning model. In cycle 1, students were able to achieve unistructural levels of 100%, and multi-structural levels of 96.43%, while in cycle 2, students were able to achieve relational levels of 42.86%, and extended abstract levels of 17.86%. The results of the analysis showed that there was a decrease in cycle 1 from the unistructural level to the multi-structural level reaching 3.57%, while in cycle 2 there was a decrease from the multi-structural level to the relational level reaching 53.57%, and from the relational level to the extended abstract level reaching 25%. This decrease can occur because the structure of the observed learning outcome taxonomy is hierarchical, so it has an increasingly complex level of abstraction at each level. The contribution of this study is that it can provide an overview of the level of students' understanding of the concept of science, both quantitatively (uni structural and multi-structural) and qualitatively (relational & extended abstract). However, the limitations of this study are that its benefits are limited only to students who are the subjects of the study.

This study assessed the effectiveness of the SOLO (Structure of Observed Learning Outcomes) Taxonomy in improving the reading comprehension proficiency of Grade 7 students. The research focused on the integration of SOLO-based questioning strategies and their impact on critical thinking and higher-order thinking skills (HOTS) in English reading tasks aligned with the MATATAG curriculum. Using a quasi-experimental design, two existing sections from a public secondary school were selected—the experimental group that the received SOLO-integrated instruction, while the control group received conventional reading instruction. A total of 64 students participated in the study. Pre-tests and post-tests were administered, with researcher-made instruments consisting of PISA-based questions and researcher-made questions aligned to SOLO levels. Results revealed a notable increase in proficiency among the experimental group from "Fair" to "Very Satisfactory," with significant gains particularly at higher SOLO levels (relational and extended abstract). In contrast, the control group remained at the "Satisfactory" level. Statistical analysis using the Wilcoxon Signed-Rank Test confirmed significant differences in performance before and after the intervention. The findings underscored the effectiveness of the SOLO framework in promoting deeper learning, metacognition, and structured cognitive progression. The study recommends integrating the SOLO Taxonomy in instructional planning, providing professional development for teachers, and aligning curriculum resources to support critical reading development. Further research is suggested to explore SOLO’s application in other subject areas and grade levels.

First-order Ordinary Differential Equation (ODE) has many applications in physics, engineering, biology, economics, and ecology. Therefore, mastering the concepts and methods of solving ODE is essential for students to be able to apply mathematics in solving real-world problems. However, the teaching of first-order ODE has not paid attention to practical applications, so that students have difficulty linking theory with real cases. This study aims to analyze the implementation of the SOLO taxonomy and Newman Error Analysis (NEA) in first-order ODE. The methodology used is a case study. The research subjects consisted of nine students of the mathematics department of FMIPA Universitas Negeri Padang. Data were collected through tests, interviews, and documentation. Then the data were analysed quantitatively and qualitatively. The results showed that there were five errors in solving first-order ODE made by students, namely Reading Errors (RE), Comprehension Errors (CE), Transformation Errors (TE), Process Skill Errors (PE), and Encoding Errors (EE). Some of the causes of these errors include students' low ability to read mathematical symbols, students' inaccuracy, not being able to use algorithms correctly, not mastering the concepts of algebra, differential, and integral, as well as not understanding in determining the systematic solution of the problem and not being accustomed to writing the final answer. This information can be used as a guideline for lecturers in designing strategies and lecture designs for first-order ODE.

The current study investigates the differences between AI-generated and human responses to ethical analyses in research ethics and integrity education, using the SOLO taxonomy to evaluate the depth of understanding. As generative AI models increasingly produce human-like responses, concerns arise regarding their impact on academic integrity and critical thinking. The findings reveal that while AI can achieve multistructural and relational levels of understanding, human responses, particularly from experts, consistently demonstrate higher levels of critical thinking and integration of ethical principles. AI responses often lack the depth and novelty characteristic of human analyses, particularly in proposing innovative solutions and considering broader implications. This suggests that reliance on AI in ethics education may hinder the development of essential skills needed for academic and professional success. The study also highlights the potential for AI to serve as a tool for evaluating student responses to learning assignments, provided transparency is maintained. Overall, educators are encouraged to design assessments that foster independent thought and creativity while integrating AI responsibly into the learning process. Future research should explore the long-term effects of AI on ethics competencies and the effectiveness of various AI platforms in educational settings.

This study aims to determine the level of thinking based on the SOLO Taxonomy on linear equation materials (SPLDV) among eighth-grade students at SMP Negeri 1 Waikabubak. The type of research used is descriptive research with a qualitative approach, and the subjects of this study are the eighth-grade students of class VIII B at SMP Negeri 1 Waikabubak, while the object of this research is the students' level of thinking in solving SPLDV story problems based on the SOLO Taxonomy. Data collection was conducted using written tests, interviews, and documentation. The collected data were analyzed through data reduction, data presentation, and conclusion drawing stages. Based on the results of the data analysis obtained, it can be concluded that the level of thinking of students on SPLDV materials based on the SOLO Taxonomy reaches different levels. According to the research results, the percentage of test results for the level of thinking of eighth-grade students class VIII B at SMP Negeri 1 Waikabubak indicates that students fall into the high category 64% of the students are in the high category, while 29% are in the medium category, and 7% are in the low category.