{"id":1317,"date":"2023-07-07T08:46:17","date_gmt":"2023-07-07T06:46:17","guid":{"rendered":"https:\/\/ifenthaler.info\/website\/?p=1317"},"modified":"2023-07-07T08:46:17","modified_gmt":"2023-07-07T06:46:17","slug":"cfp-metacognition-and-self-directed-learning-skills-for-self-assessment-in-technology-based-learning","status":"publish","type":"post","link":"https:\/\/ifenthaler.info\/website\/?p=1317","title":{"rendered":"CfP: Metacognition\u00a0and self-directed learning: Skills\u00a0for\u00a0self-assessment\u00a0in\u00a0technology-based learning"},"content":{"rendered":"\n

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 \u201ca 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\u201d (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).  <\/p>\n\n\n\n

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.  <\/p>\n\n\n\n

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.  <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

Possible topics of interest include, but are not limited to, the following. <\/p>\n\n\n\n