TY - JOUR A1 - Münchow, Hannes A1 - Mengelkamp, Christoph A1 - Bannert, Maria T1 - The better you feel the better you learn: do warm colours and rounded shapes enhance learning outcome in multimedia learning? JF - Education Research International N2 - The aim of the present study was to examine whether fostering positive activating affect during multimedia learning enhances learning outcome. University students were randomly assigned to either a multimedia learning environment designed to induce positive activating affect through the use of “warm” colours and rounded shapes () or an affectively neutral environment that used achromatic colours and sharp edges (). Participants learned about the topic of functional neuroanatomy for 20 minutes and had to answer several questions for comprehension and transfer afterwards. Affective states as well as achievement goal orientations were investigated before and after the learning phase using questionnaires. The results show that participants in the affectively positive environment were superior in comprehension as well as transfer when initial affect was strong. Preexperimental positive affect was therefore a predictor of comprehension and a moderator for transfer. Goal orientations did not influence these effects. The findings support the idea that positive affect, induced through the design of the particular multimedia learning environment, can facilitate performance if initial affective states are taken into account. KW - shape KW - learning outcome KW - multimedia learning KW - colour Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-158566 VL - 2017 ER - TY - JOUR A1 - Sonnenberg, Christoph A1 - Bannert, Maria T1 - Evaluating the Impact of Instructional Support Using Data Mining and Process Mining: A Micro-Level Analysis of the Effectiveness of Metacognitive Prompts JF - Journal of Educational Data Mining N2 - In computer-supported learning environments, the deployment of self-regulatory skills represents an essential prerequisite for successful learning. Metacognitive prompts are a promising type of instructional support to activate students’ strategic learning activities. However, despite positive effects in previous studies, there are still a large number of students who do not benefit from provided support. Therefore, it may be necessary to consider explicitly the conditions under which a prompt is beneficial for a student, i.e., so-called adaptive scaffolding. The current study aims to (i) classify the effectiveness of prompts on regulatory behavior, (ii) investigate the correspondence of the classification with learning outcome, and (iii) discover the conditions under which prompts induce regulatory activities (i.e., the proper temporal positioning of prompts). The think-aloud data of an experiment in which metacognitive prompts supported the experimental group (n = 35) was used to distinguish between effective and non-effective prompts. Students’ activities preceding the prompt presentation were analyzed using data mining and process mining techniques. The results indicate that approximately half of the presented prompts induced metacognitive learning activities as expected. Moreover, the number of induced monitoring activities correlates positively with transfer performance. Finally, the occurrence of orientation and monitoring activities, which are not well-embedded in the course of learning, increases the effectiveness of a presented prompt. In general, our findings demonstrate the benefits of investigating metacognitive support using process data, which can provide implications for the design of effective instructional support. KW - process mining KW - think-aloud data KW - metacognitive prompting KW - micro-level analysis KW - instructional support KW - self-regulated learning Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-152375 UR - http://www.educationaldatamining.org/JEDM/index.php/JEDM/article/view/JEDM2016-8-2-3 N1 - Dieser Artikel ist auch Bestandteil der Dissertation: Sonnenberg, Christoph: Analyzing Technology-Enhanced Learning Processes: What Can Process Mining Techniques Contribute to the Evaluation of Instructional Support?. - Würzburg, Univ., Diss., 2017. - [online]. URN: urn:nbn:de:bvb:20-opus-152354 VL - 8 IS - 2 ER - TY - JOUR A1 - Pieger, Elisabeth A1 - Mengelkamp, Christoph A1 - Bannert, Maria T1 - Disfluency as a Desirable Difficulty — The Effects of Letter Deletion on Monitoring and Performance JF - Frontiers in Education N2 - Desirable difficulties initiate learning processes that foster performance. Such a desirable difficulty is generation, e.g., filling in deleted letters in a deleted letter text. Likewise, letter deletion is a manipulation of processing fluency: A deleted letter text is more difficult to process than an intact text. Disfluency theory also supposes that disfluency initiates analytic processes and thus, improves performance. However, performance is often not affected but, rather, monitoring is affected. The aim of this study is to propose a specification of the effects of disfluency as a desirable difficulty: We suppose that mentally filling in deleted letters activates analytic monitoring but not necessarily analytic cognitive processing and improved performance. Moreover, once activated, analytic monitoring should remain for succeeding fluent text. To test our assumptions, half of the students (n = 32) first learned with a disfluent (deleted letter) text and then with a fluent (intact) text. Results show no differences in monitoring between the disfluent and the fluent text. This supports our assumption that disfluency activates analytic monitoring that remains for succeeding fluent text. When the other half of the students (n = 33) first learned with a fluent and then with a disfluent text, differences in monitoring between the disfluent and the fluent text were found. Performance was significantly affected by fluency but in favor of the fluent texts, and hence, disfluency did not activate analytic cognitive processing. Thus, difficulties can foster analytic monitoring that remains for succeeding fluent text, but they do not necessarily improve performance. Further research is required to investigate how analytic monitoring can lead to improved cognitive processing and performance. KW - metacomprehension KW - disfluency KW - metacognitive monitoring KW - metacognitive control KW - metacognitive judgments KW - desirable difficulties Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197179 SN - 2504-284X VL - 3 IS - 101 ER - TY - JOUR A1 - Sonnenberg, Christoph A1 - Bannert, Maria T1 - Discovering the Effects of Metacognitive Prompts on the Sequential Structure of SRL-Processes Using Process Mining Techniques JF - Journal of Learning Analystics N2 - According to research examining self‐regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing regulatory activities, which, in turn, improve the learning outcome. However, the specific effects of metacognitive support on the dynamic characteristics of SRL are not understood. Therefore, the aim of our study was to analyze the effects of metacognitive prompts on learning processes and outcomes during a computer‐based learning task. Participants of the experimental group (EG, n=35) were supported by metacognitive prompts, whereas participants of the control group (CG, n=35) received no support. Data regarding learning processes were obtained by concurrent think‐aloud protocols. The EG exhibited significantly more metacognitive learning events than did the CG. Furthermore, these regulatory activities correspond positively with learning outcomes. Process mining techniques were used to analyze sequential patterns. Our findings indicate differences in the process models of the EG and CG and demonstrate the added value of taking the order of learning activities into account by discovering regulatory patterns. KW - HeuristicsMiner algorithm KW - self‐regulated learning KW - metacognitive prompting KW - process analysis KW - process mining KW - think‐aloud data Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-152362 UR - http://learning-analytics.info/journals/index.php/JLA/article/view/4090 SN - 1929‐7750 N1 - Dieser Artikel ist auch Bestandteil der Dissertation: Sonnenberg, Christoph: Analyzing Technology-Enhanced Learning Processes: What Can Process Mining Techniques Contribute to the Evaluation of Instructional Support?. - Würzburg, Univ., Diss., 2017. - [online]. URN: urn:nbn:de:bvb:20-opus-152354 VL - 2 IS - 1 ER -