Metacognition is understood as knowledge about one’s own knowledge, i.e., person-related knowledge, task-related knowledge, strategic knowledge, and the regulation of one’s own cognitive activity (Flavell, 1979). This means above all the monitoring and control of one’s own cognitive processes. Metacognitive skills are fundamental for successful self-assessment. Self-directed learning is defined as “a process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies and evaluating learning outcomes” (Knowles, 1975, p. 18). This definition includes requirements for a good self-assessment such as autonomy, organisation skills, self-discipline, effective communication, acceptance of constructive feedback, and engagement in self-evaluation and self-reflection. This supports the relationship between self-assessment and self-directed learning (Lubbe & Mentz, 2021) and points to the value of student agency whereby studentsdetermine how their learning and assessment will take place (Tlili et al., 2022).
There is a wide range of conceptions about self-assessment (Andrade, 2019), from self-assessment for grading in summative form to self-assessment as self-feedback in formative nature (Panadero et al., 2016). Generally, self-assessment has a positive impact on learning performance (Yan et al., 2023). But there are some challenges. It is somewhat problematic in summative assessments, as it should be ensured that students are able to evaluate their performance sufficiently well with enough honesty, especially when they perform badly (e.g., Andrade, 2019; Tejeiro et al., 2012). Accordingly, self-assessment is mainly used in formative tests and tasks, e.g., to let students see for themselves where they have gaps in their knowledge or, more generally, areas that still need to be improved (e.g., Seifried, & Spinath, 2021; Panadero et al., 2019). But one of the critical points in self-assessment is whether students have these necessary skills.
There is a broad range of possible applications of self-assessment in technology-based learning. So, self-assessment is also well suited for adaptive tasks (e.g., Benchoff et al., 2018) using learning analytics and artificial intelligence in which, for example, the current knowledge or the difficulty of questions play a role.
The scope of the special issue is to bring together theoretical models and empirical research about metacognitive skills and self-directed learning skills necessary for successful self-assessment in technology-based learning. Important related topics are adaptive learning, learning analytics, artificial intelligence and feedback design.
Possible topics of interest include, but are not limited to, the following.
- The role of metacognition for self-assessment
- The role of self-directed learning for self-assessment
- Metacognition / self-directed learning and self-assessment in technology-based learning, e.g., adaptive learning designs, feedback designs
- Metacognitive skills and self-directed learning skills for self-assessment
- Learning analytics and artificial intelligence to facilitate self-assessment
- Problematic aspects and interventions in self-assessment
- The role of self-directed learning in the creation of renewable assessment
The Research Topic welcomes original research articles, systematic reviews of the literature, and conceptual analysis papers. Research employing qualitative or mixed method research designs are similarly encouraged. Research articles that employ secondary data analyses are also welcome.
Interested authors are asked to submit the manuscript title and an abstract of up to 500-1000 words (excluding references and tables), with a short bio of authors (150 words maximum per author) to sdl@ffhs.ch.
For any inquiries about the appropriateness of contribution topics, please contact the special issue guest editors Prof. Dr. Egon Werlen at egon.werlen@ffhs.ch or Prof. Dr. Dorothy Laubscher at dorothy.laubscher@nwu.ac.za.
Abstracts will be reviewed, and selected authors will be invited to submit a full manuscript for consideration for inclusion in the special issue in Technology, Knoweldge and Learning (https://www.springer.com/journal/10758).
References
Andrade, H. L. (2019). A critical review of research on student self-assessment. Frontiers in Education, 4, 87. https://doi.org/10.3389/feduc.2019.00087
Benchoff, D. E., Gonzalez, M. P., & Huapaya, C. R. (2018). Personalization of tests for formative self-assessment. IEEE Revista Iberoamericana de Tecnologías delAprendizaje, 13(2), 70-74. https://doi.org/10.1109/RITA.2018.2831759
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American psychologist, 34(10), 906-911. https://doi.org/10.1037/0003-066X.34.10.906
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Association Press.
Lubbe, A., & Mentz, E. (2021). Self-directed learning-oriented assessment and assessment literacy: Essential for 21st century learning. In E. Mentz, & A. Lubbe (Eds.).Learning through assessment: An approach towards self-directed learning (pp. 1-25). AOSIS. https://doi.org/10.4102/aosis.2021.BK280.01
Panadero, E., Brown, G. T., & Strijbos, J. W. (2016). The future of student self-assessment: A review of known unknowns and potential directions. Educational psychology review, 28, 803-830. https://doi.org/10.1007/s10648-015-9350-2
Panadero, E., Lipnevich, A., & Broadbent, J. (2019). Turning self-assessment into self-feedback. In M. Henderson, R. Ajjawi, D. Boud, & E. Molloy, (Eds.). The impact of feedback in higher education: Improving assessment outcomes for learners (pp. 147-163). Springer Nature. https://doi.org/10.1007/978-3-030-25112-3_9
Seifried, E., & Spinath, B. (2021). Using formative self-assessment to improve teaching and learning in educational psychology courses. In S. A. Nolan, C. M. Hakala, & R. E. Landrum (Eds.), Assessing undergraduate learning in psychology: Strategies for measuring and improving student performance (pp. 161–176). American Psychological Association. https://doi.org/10.1037/0000183-012
Tejeiro, R. A., Gomez-Vallecillo, J. L., Romero, A. F., Pelegrina, M., Wallace, A., & Emberley, E. (2012). Summative self-assessment in higher education: Implications of its counting towards the final mark. Electronic Journal of Research in Educational Psychology, 10(2), 789-812.
Tlili, A., Burgos, D., Olivier, J., & Huang, R. (2022). Self-directed learning and assessment in a crisis context: the COVID-19 pandemic as a case study. Journal of e-Learning and Knowledge Society, 18(2), pp.1-10. https://doi.org/10.20368/1971-8829/1135475
Yan, Z., Wang, X., Boud, D., & Lao, H. (2023). The effect of self-assessment on academic performance and the role of explicitness: a meta-analysis. Assessment & Evaluation in Higher Education, 48(1), 1-15. https://doi.org/10.1080/02602938.2021.2012644
Timeline
- 15.08.2023 Call for contributions to the special Issue
- 30.09.2023 Deadline for abstract submissions
- 15.11.2023 Feedback to abstracts (invitation to submit a paper)
- 30.04.2024 Deadline for paper submissions
- 30.06.2024 End of peer reviews (two rounds)
- 31.07.2024 Final decision of acceptance / rejection
- October 2024 Publication of the special issue
