Does the Accuracy Matter? Accurate Concept Map Feedback Helps Students Improve the Cohesion of Their Explanations
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| Title: | Does the Accuracy Matter? Accurate Concept Map Feedback Helps Students Improve the Cohesion of Their Explanations |
|---|---|
| Language: | English |
| Authors: | Lachner, Andreas (ORCID |
| Source: | Educational Technology Research and Development. Oct 2018 66(5):1051-1067. |
| Availability: | Springer. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2018 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Accuracy, Concept Mapping, Feedback (Response), Writing (Composition), Connected Discourse, Writing Evaluation, Writing Improvement, Outcomes of Education, Computer Assisted Instruction |
| DOI: | 10.1007/s11423-018-9571-4 |
| ISSN: | 1042-1629 |
| Abstract: | Students are often challenged by the demand of writing cohesive explanatory texts. Prior research has shown that providing students with concept map feedback that visualizes explanatory cohesion deficits helped students generate more cohesive explanations. We conducted an experiment to investigate whether the accuracy of the provided information within the concept map feedback affected students' improvements of cohesion. Accordingly, we varied the represented accuracy of information within such concept maps: Students either received accurate concept map feedback that depicted the real relations between concepts, as well as the authentic cohesion gaps in their explanations, or students received inaccurate concept map feedback, which depicted randomly drawn relations and random cohesion gaps. Additionally, in a baseline condition, students did not receive any feedback. We found that the students in the accurate feedback condition generated more cohesive explanations than the students in the no-feedback condition, whereas the students in the inaccurate feedback condition lay in-between. Evidently, providing feedback in general can be regarded as beneficial to enhance students' writing. However, the accuracy of the provided feedback further impacts the effectiveness of computer-generated concept maps. |
| Abstractor: | As Provided |
| Number of References: | 41 |
| Entry Date: | 2018 |
| Accession Number: | EJ1190144 |
| Database: | ERIC |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwFbICfUtfz-5qISxkl0S-SWAAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDAEwHqurXHImX7Ml4wIBEICBmhMmEJECm0av7BHRal9KyCzA8HqfIq2_XOQ7F7BLsGWn6Bo5JcjRHc9WXMPAuI2aypRztqsJqgrrNeLWQ9QUXZe8x4Zmionm0QRVDiqeNAFIMqdmDZUhiGAHL95G4KeFfb8HD9fuxehzwUspn7mRV3tz4QWKv12ksvxYEomGs69-d-vbSY67pAmZ18qT3pT_cP14iwArKYm9sjA= Text: Availability: 1 Value: <anid>AN0131619156;etr01oct.18;2018Sep07.11:33;v2.2.500</anid> <title id="AN0131619156-1">Does the accuracy matter? Accurate concept map feedback helps students improve the cohesion of their explanations </title> <p>Students are often challenged by the demand of writing cohesive explanatory texts. Prior research has shown that providing students with concept map feedback that visualizes explanatory cohesion deficits helped students generate more cohesive explanations. We conducted an experiment to investigate whether the accuracy of the provided information within the concept map feedback affected students’ improvements of cohesion. Accordingly, we varied the represented accuracy of information within such concept maps: Students either received accurate concept map feedback that depicted the real relations between concepts, as well as the authentic cohesion gaps in their explanations, or students received inaccurate concept map feedback, which depicted randomly drawn relations and random cohesion gaps. Additionally, in a baseline condition, students did not receive any feedback. We found that the students in the accurate feedback condition generated more cohesive explanations than the students in the no-feedback condition, whereas the students in the inaccurate feedback condition lay in-between. Evidently, providing feedback in general can be regarded as beneficial to enhance students’ writing. However, the accuracy of the provided feedback further impacts the effectiveness of computer-generated concept maps.</p> <p>Computer-based feedback; Writing; Cohesion; Concept maps</p> <hd id="AN0131619156-2">Cohesion as a critical text feature of supporting readers’ comprehension</hd> <p>Through the use of modern communication technologies, such as chats, e-mails, wikis, or blogs, writing has become a ubiquitous way of conveying subject matter information to others (National Commission on Writing [<reflink idref="bib27" id="ref1">27</reflink>] ; Rowan [<reflink idref="bib34" id="ref2">34</reflink>] ). However, to make such texts comprehensible to potential readers, writers need to consider certain text characteristics (e.g., syntax, word difficulty, or cohesion; see McNamara [<reflink idref="bib20" id="ref3">20</reflink>] , for details). Especially for writing explanatory texts, research showed that the cohesion of a text in particular plays a crucial role to enhance the comprehensibility of a text (Graesser et al. [<reflink idref="bib8" id="ref4">8</reflink>] ; McNamara and Kintsch [<reflink idref="bib22" id="ref5">22</reflink>] ; Wittwer and Ihme [<reflink idref="bib41" id="ref6">41</reflink>] ).</p> <p>Cohesion can be defined as the extent to which relations between concepts of a text become explicit (McNamara et al. [<reflink idref="bib23" id="ref7">23</reflink>] ). Cohesion can be achieved either syntactically by inserting connectives (e.g., therefore, and, because), or semantically by reiterating arguments, using near-synonyms, or by inserting bridging information, which explicitly explains the semantic relation between two neighboring sentences (Halliday and Hasan [<reflink idref="bib11" id="ref8">11</reflink>] ; McNamara et al. [<reflink idref="bib23" id="ref9">23</reflink>] ). In several studies, high-cohesive texts have been shown to facilitate students’ text comprehension processes, as students were more able to relate subsequent information within the text, as compared to students who learnt with low-cohesive texts (Ozuru et al. [<reflink idref="bib28" id="ref10">28</reflink>] ). After learning with high-cohesive texts, these deeper comprehension processes also resulted in students’ higher learning outcomes (Hall et al. [<reflink idref="bib10" id="ref11">10</reflink>] ; McNamara and Kintsch [<reflink idref="bib22" id="ref12">22</reflink>] ).</p> <hd id="AN0131619156-3">Students’ difficulties in writing cohesive texts</hd> <p>Despite the crucial role of cohesion in fostering a reader’s comprehension of explanatory texts, students often struggle with the demand of writing cohesive explanatory texts that convey the intended information in a comprehensible manner (Concha and Paratore [<reflink idref="bib5" id="ref13">5</reflink>] ; Lachner and Nückles [<reflink idref="bib18" id="ref14">18</reflink>] ). For instance, Concha and Paratore ([<reflink idref="bib5" id="ref15">5</reflink>] ) analyzed Chilean college students’ strategies to establish cohesion, and asked them to write an argumentative text about the importance of English as a second language. The authors found that students were able to use simple cohesive ties such as connectives. However, the students failed to relate neighboring sentences using more sophisticated cohesion mechanisms, which are considered to be crucial for establishing cohesion (e.g., by using common noun phrases, or including bridging information).</p> <hd id="AN0131619156-4">Computer-based formative feedback on students’ cohesive writing</hd> <p>The findings by Concha and Paratore ([<reflink idref="bib5" id="ref16">5</reflink>] ) suggest that students would need instructional support in order to improve the cohesion of their written explanatory texts. Graham et al. ([<reflink idref="bib9" id="ref17">9</reflink>] ) emphasized the importance of formative feedback to enhance students’ writing skills in their meta-analysis. Accordingly, formative feedback on the students’ written products seems to be an especially fruitful and advantageous approach to support students in developing their writing (Hattie and Timperley [<reflink idref="bib12" id="ref18">12</reflink>] ; Molloy and Boud [<reflink idref="bib24" id="ref19">24</reflink>] ). Formative feedback can be defined as information which is provided by an agent (e.g., teacher, peer, or a computer) with regard to distinct aspects of students’ writing performance, such as text cohesion (Hattie and Timperley [<reflink idref="bib12" id="ref20">12</reflink>] ). For that purpose, Hattie and Timperley ([<reflink idref="bib12" id="ref21">12</reflink>] ) proposed that effective feedback should both contain information (a) about the current goal of a task (e.g., the goal to write a cohesive text), (b) about the current level of performance (e.g., the level of cohesion of a text), as well as (c) strategies to improve one’s performance (e.g., strategies to enhance the cohesion of one’s draft).</p> <hd id="AN0131619156-5">Deficits of computer-based feedback</hd> <p>However, as providing formative feedback on students’ writing products is generally very laborious and time-consuming (Cho and MacArthur [<reflink idref="bib3" id="ref22">3</reflink>] ), computer-based feedback technologies might be a useful alternative (Pirnay-Dummer et al. [<reflink idref="bib30" id="ref23">30</reflink>] ; Sung et al. [<reflink idref="bib38" id="ref24">38</reflink>] ). For instance, in their meta-analysis, Graham et al. ([<reflink idref="bib9" id="ref25">9</reflink>] ) investigated the effects of computer-based feedback on students’ quality of writing. They reported a non-significant average weighted effect of 0.29 (small to medium effect), indicating that computer-based feedback was only slightly effective to scaffold students’ writing. However, it has to be noted that this comparison was only based on four empirical studies, which potentially restricts the statistical power of the analyses. Nevertheless, this finding by Graham et al. implies that the provision of computer-based feedback does not necessarily lead to improvements of students’ writing skills, but that its effectiveness largely depends on the quality of the implemented computer-based feedback. For instance, one potential restriction of computer-based feedback could be that it relies on technologies, such as natural language processing algorithms or machine learning, which may not be as capable to process complex linguistic information and detect deficits of semantic features of natural text corpora (e.g., the level of cohesion) as compared to human expert raters. This, however, may impair the accuracy of the provided feedback (Kellogg and Whiteford [<reflink idref="bib14" id="ref26">14</reflink>] ). For instance, Reilly et al. ([<reflink idref="bib32" id="ref27">32</reflink>] ) analyzed the accuracy of an automated essay-scoring technology (AES), and compared these ratings with human expert ratings in a large-scale MOOCs environment. The authors found low to medium percentage agreements between the AES-scores and the scores by the human raters (for similar findings, see McNamara et al. [<reflink idref="bib21" id="ref28">21</reflink>] ), indicating rather low levels of feedback accuracy of the AES feedback technology.</p> <hd id="AN0131619156-6">Effective computer based feedback approaches to scaffold students’ writing</hd> <p>Nevertheless, recent studies documented valid approaches to automatically provide feedback on students’ writing with rather high levels of feedback accuracy. For instance, Writing-Pal is an intelligent tutoring system that helps students revise and improve their texts for distinct text features, such as cohesion (Roscoe and McNamara [<reflink idref="bib33" id="ref29">33</reflink>] ). In addition to direct strategy instruction, Writing-Pal provides computer-based feedback on students’ drafts with regard to indicators of text quality (e.g., essay length, structure, elaboration, or relevance of the topic). The feedback consists of a holistic overall rating for essay quality from poor to great (6-point scale) and a number of prompt-like recommendations based on the deficits identified by the system (such as: “One way to expand your essay is to add additional relevant examples and evidence”, see Roscoe and McNamara [<reflink idref="bib33" id="ref30">33</reflink>] ). Writing-Pal has been proved to be highly valid, as the Writing-Pal indicators showed high-percentage agreement with human expert-ratings (McNamara et al. [<reflink idref="bib21" id="ref31">21</reflink>] ). Furthermore, in a longitudinal study, Roscoe and McNamara ([<reflink idref="bib33" id="ref32">33</reflink>] ) showed that students’ quality of writing improved over time as they worked with Writing-Pal. However, students reported that they had difficulties with the generality of the information provided within the feedback, as nearly 25% of the students criticized the automated Writing-Pal feedback as being too general and thus difficult to apply.</p> <p>Against this background, Lachner et al. ([<reflink idref="bib17" id="ref33">17</reflink>] ) developed CohViz which provides students with specific graphical information that helps them localize cohesion problems in their drafts and thereby may trigger students’ goal-directed revision activities (for similar approaches, see Kim [<reflink idref="bib15" id="ref34">15</reflink>] ; Pirnay-Dummer et al. [<reflink idref="bib30" id="ref35">30</reflink>] ). To provide formative and specific feedback on students’ explanation drafts, CohViz generates concept-map-like representations from written explanations. Providing feedback in a graphical modality was chosen, as the integration of graphical and textual representations may additionally enhance students’ writing performance (Ainsworth [<reflink idref="bib1" id="ref36">1</reflink>] ; Narciss [<reflink idref="bib25" id="ref37">25</reflink>] ). As such, concept map feedback should particularly assist students to exploit their perceptual processes, and facilitate students’ search processes (Ainsworth [<reflink idref="bib1" id="ref38">1</reflink>] ; Larkin and Simon [<reflink idref="bib19" id="ref39">19</reflink>] ), such as the search of cohesion gaps within a text.</p> <p>To that end, by using recent natural language processing technologies, the students’ texts are segmented into individual propositions and visualized as a node-link structure (see Materials-section, for more details). Nodes represent the concepts of the explanation, and links with arrows indicate the structural relationships between these concepts based on their grammatical function (e.g., subject, object) within a sentence. The concept maps provide a writer with specific feedback about the cohesion of their draft (see Fig. 1b for an example). The concept map depicts all the concepts and relations of an explanation. More importantly, cohesion gaps are represented as unrelated fragments in the concept map (e.g., the concept map in Fig. 1b contained three cohesion gaps). Thus, writers can use the concept map to improve the cohesion of their explanation by monitoring their draft for potential cohesion gaps (Berlanga et al. [<reflink idref="bib2" id="ref40">2</reflink>] ). As for Writing-Pal, the concept maps generated with the visualization tool have shown to be highly valid in comparison to human expert raters (Lachner et al. [<reflink idref="bib17" id="ref41">17</reflink>] ). Beyond that, Lachner et al. ([<reflink idref="bib16" id="ref42">16</reflink>] ) also investigated whether such formative concept map feedback would help students improve the cohesion of their written explanations. The authors asked students to provide a draft of an instructional explanation on cognitive load theory. Afterwards, the students either received concept map feedback or no feedback. The authors found that students who received automated concept map feedback for revising their drafts wrote explanations that were more cohesive, and subsequently more comprehensible than the explanations of students who had revised their drafts without concept map feedback. In a follow-up-study, Lachner et al. ([<reflink idref="bib17" id="ref43">17</reflink>] ) directly compared the effectiveness of the concept map feedback to other feedback modalities (see Narciss [<reflink idref="bib25" id="ref44">25</reflink>] ) in an ecologically valid lecture-experiment with teacher students. For that purpose, students were randomly provided either with concept map feedback or outline feedback, the latter presenting the identical information but in a linear textual format. Additionally, the authors included a control group of students who only received a prompt to revise for cohesion and the amount of their cohesion gaps.Examples of automated concept maps for the accurate concept map condition (a), and the inaccurate concept map condition (b) of an excerpt of a translated student’s draft</p> <p>The authors found that the concept map feedback induced the lowest level of cognitive load while revising, as compared to the general feedback or the outline feedback. Additionally, students who received specific feedback (i.e., concept map or outline feedback) wrote explanations that were more cohesive as compared to students with general feedback. Hence, one may conclude that the concept map feedback is an efficient approach to support students’ revisions for cohesion, as the concept map feedback was as effective as the outline feedback, but, at the same time, inflicted lower levels of cognitive load during writing.</p> <hd id="AN0131619156-7">Impact of feedback accuracy on students’ writing</hd> <p>Given that computer-based feedback approaches largely vary with regard to the accuracy of the provided feedback information, the question arises, whether and how the accuracy of computer-based feedback impacts students’ writing performance. On the one hand, it can be assumed that students depend on the accuracy of the feedback, as students would need the specific and authentic information (e.g., information about the allocation of cohesion deficits) in order to successfully resolve the described deficits within the feedback (Shute [<reflink idref="bib37" id="ref45">37</reflink>] ). On the other hand, it can be assumed that simply the availability of computer-based feedback would suffice to raise students’ metacognitive awareness towards deficits of their text, and simply act as a stimulus telling the students to look for deficits in their drafts, regardless of the accuracy of the feedback. However, empirical evidence on effects of the accuracy of feedback is scarce.</p> <p>For instance, in a correlational study, Gielen et al. ([<reflink idref="bib7" id="ref46">7</reflink>] ) investigated the effects of distinct (peer)-feedback characteristics on students’ improvements of writing. For that purpose, they implemented peer-feedback in two secondary education classes, and analyzed effects of natural feedback characteristics (e.g., complexity, accuracy, justifications) on students’ writing performance. The authors found that primarily the specificity of the peers’ justifications affected students’ writing performance, whereas the accuracy of the provided feedback had no further effects on students’ writing. However, it has to be noted that these findings were based only on correlational data, which restricts the causality of the findings. For instance, it could be the case that although feedback accuracy had beneficial effects on students’ writing, there were additional co-occurring feedback features which simultaneously impaired students’ writing performance and as such canceled out the overall effect of feedback accuracy.</p> <p>Following an experimental approach, Hirst et al. ([<reflink idref="bib13" id="ref47">13</reflink>] ) experimentally varied the accuracy of the feedback, and either provided students with inaccurate feedback or accurate feedback while learning the concepts of artificial shapes. The authors found that inaccurate feedback was more detrimental for students’ learning than accurate feedback, even when students were provided with additional corrective feedback in a subsequent learning phase. In a similar study, Wäschle et al. ([<reflink idref="bib40" id="ref48">40</reflink>] ) provided students during a whole semester either with (a) accurate graphical feedback; (b) inaccurate graphical feedback, or (c) no feedback about their current procrastination tendencies to help them regulate their procrastination behavior. In the accurate graphical feedback condition, students’ authentic levels of the procrastination scale were visualized in a line-chart. The inaccurate graphical feedback, in contrast, displayed a line chart which was based on computer-generated data. Additionally, to analyze over-all effects of the provision of computer-based feedback, a control group of students did not receive any feedback. The authors found that the students with the accurate feedback were able to reduce their levels of procrastination more than students without feedback, while students with inaccurate feedback lay in-between. These findings indicate that accurate feedback was more effective than the inaccurate feedback, but that the provision of feedback about procrastination tendencies itself had additional effects on students’ reduction of procrastination behavior, regardless of the accuracy of the feedback.</p> <p>Together, findings with regard to feedback accuracy were either based on correlational data which do not imply causality, or were investigated in a learning domain very different from writing (i.e., procrastination behavior, recognition of artificial shapes). Thus, the question remains, whether and how these findings would generalize to computer-based feedback approaches on students’ writing.</p> <hd id="AN0131619156-8">Overview of the study</hd> <p>Against this background, we conducted an experimental study in which we investigated effects of the accuracy of computer-based feedback on students’ writing cohesive explanatory texts. In contrast to other writing studies which followed a correlational approach (e.g., Gielen et al. [<reflink idref="bib7" id="ref49">7</reflink>] ; Patchan et al. [<reflink idref="bib29" id="ref50">29</reflink>] ), conducting an experimental study allowed us to establish a causal link between the accuracy of concept map feedback and students’ writing performance. For that purpose, we used CohViz by Lachner et al. ([<reflink idref="bib17" id="ref51">17</reflink>] ), as it showed high percentage agreements with human expert ratings, and provided students either with (a) accurate concept map feedback; (b) inaccurate concept map; or (c) without any feedback during their revisions. In the accurate feedback condition, the concept map represented the authentic concepts and authentic relations of a student’s explanatory draft, as well as the cohesion gaps generated by CohViz (see Fig. 1b). As for the accurate concept map condition, the inaccurate concept map depicted the authentic concepts of a student’s explanation to provide a valid anchor for student’s processing of the inaccurate concept map feedback. Nevertheless, the relations as well as the fragments between the concepts were randomly drawn by a computer program (for more details, see Methods section). Thus, the depicted cohesion gaps (i.e., the level of cohesion), as well as the relations between the concepts were generated by chance, and distorted the validity of the information within the concept map feedback (for similar approaches, see Wäschle et al. [<reflink idref="bib40" id="ref52">40</reflink>] ). The students in the no-feedback condition, in contrast, did not receive any feedback for their revision, but were nevertheless asked to revise their explanation for cohesion, to obtain overall effects of the provision of feedback on students’ writing.</p> <p>Following Wäschle et al. ([<reflink idref="bib40" id="ref53">40</reflink>] ), we expected that accurate concept map feedback should be more beneficial than inaccurate concept map feedback, whereas no feedback should be least effective. Therefore, students with accurate concept map feedback should show higher levels of cohesion in their revised instructional explanations (i.e., fewer cohesion gaps) as compared with the students who received inaccurate concept map feedback, or students who received no feedback. Nevertheless, as the mere provision of feedback could raise students’ meta-cognitive awareness towards potential cohesion gaps in their drafts (Wäschle et al. [<reflink idref="bib40" id="ref54">40</reflink>] ), students with inaccurate feedback should produce more cohesive explanations than students without feedback.</p> <hd id="AN0131619156-9">Method</hd> <hd id="AN0131619156-10">Participants</hd> <p>Sixty advanced Educational Science students from a German university participated in the study. Unfortunately, we had to exclude two participants from further analyses because their revised explanatory texts were not stored in the database due to computer problems,. The mean age of the remaining 58 participants was 22.62 (SD = 2.80). 74.60% of the participants were female. The students were in their fourth semester on average (SD = 0.94), and possessed substantial topic knowledge in cognitive load theory (see Table 1), and medium teaching experience with M = 3.14 (SD = 1.00, on a 5 point Likert scale). All students had attended an introductory course in Educational Psychology beforehand, in which they had been introduced to cognitive load theory and to the use of concept maps as a learning strategy. None of them had yet attended a writing course. The students participated in exchange for course credit.</p> <hd id="AN0131619156-11">Design</hd> <p>Students were randomly assigned to one of three experimental conditions. A repeated measures design with two measuring points was used with type of feedback as independent variable: (a) Students only received a prompt for revising their explanation in order to improve its cohesion (no concept map feedback group). (b) Students were provided with a prompt and additionally received an accurate concept map representing the conceptual structure and cohesion gaps of their explanation draft (accurate concept map feedback group). (c) Students were provided with a prompt and additionally received an inaccurate concept map representing the authentic concepts, but with randomly drawn relations (inaccurate concept map feedback group). The level of cohesion was defined as within-subjects variable with two measuring points: (<reflink idref="bib1" id="ref55">1</reflink>) level of cohesion of the draft, and (<reflink idref="bib2" id="ref56">2</reflink>) level of cohesion of the revised explanation. Additionally, we measured students’ prior topic knowledge about cognitive load theory.</p> <hd id="AN0131619156-12">Materials</hd> <hd id="AN0131619156-13">Prior topic knowledge test</hd> <p>We used an adapted knowledge test by Lachner et al. ([<reflink idref="bib17" id="ref57">17</reflink>] ) to assess students’ understanding of cognitive load theory, which was the topic of our explanation task. The test consisted of five multiple-choice items with four answer possibilities and one correct solution (e.g., “What is the split attention effect?”). The students received one point for each correctly answered question, resulting in a possible total score of 5. The average reliability was acceptable, ω = .52 (McDonalds ω), as the multiple choice test measured different aspects of cognitive load theory.</p> <hd id="AN0131619156-14">CohViz</hd> <hd id="AN0131619156-15">Accurate concept map condition</hd> <p>To generate the concept maps for the accurate concept map condition, we used CohViz developed by Lachner et al. ([<reflink idref="bib16" id="ref58">16</reflink>] , [<reflink idref="bib17" id="ref59">17</reflink>] ). CohViz generates a concept map from text via a four-step procedure (see Fig. 1). First, CohViz imports a text and stores each sentence separately (Fig. 1a). Second, all concepts (i.e., nouns) and their grammatical functions (i.e., subject, possessor, direct object, and indirect object) are determined and extracted (see Fig. 1a, extraction) by using a natural language processing technology called RFTagger (Schmid and Laws [<reflink idref="bib35" id="ref60">35</reflink>] ). Third, the relations between the concepts are drawn according to their grammatical function (van Valin [<reflink idref="bib39" id="ref61">39</reflink>] ): (a) subject antecedes possessor, (b) subject antecedes direct objects, (c) subject antecedes indirect object, and (d) direct object antecedes indirect object. These relations are then stored in a list containing the sequences of concepts, such as “concept x → concept y” (see Fig. 1a, relation). These sequences of concepts and their relations are visualized as a concept map (see Fig. 1b). These concept maps have been shown to validly capture characteristics of cohesion, as the indicators derived from the concept map highly correlate with human expert ratings (see Lachner et al. [<reflink idref="bib17" id="ref62">17</reflink>] ).</p> <hd id="AN0131619156-16">Inaccurate concept map condition</hd> <p>For the inaccurate concept map feedback condition (see Fig. 1c), as for the accurate concept map feedback condition, the concepts of the students’ texts were extracted first. Second, a computer algorithm randomly determined the number of fragments (i.e., the number of cohesion gaps within the concept maps) for each individual random concept map. To increase the readability of the inaccurate concept maps and make the inaccurate concept maps as authentic as possible, we determined the range of number of fragments depicted within the inaccurate concept maps. For that purpose, we aligned the range of the number of fragments within the inaccurate concept maps to the empirical values in the study by Lachner et al. ([<reflink idref="bib16" id="ref63">16</reflink>] ). In that study, the students’ concept maps depicted M = 2.95 (SD = 1.47) fragments in their concept maps on average. Accordingly, we set the range of fragments of the inaccurate concept maps to between one and four so that the number of fragments in our inaccurate concept maps would roughly correspond to values within one standard deviation to either side of the mean of fragments of the study by Lachner et al. ([<reflink idref="bib17" id="ref64">17</reflink>] ). Third, the concepts within the concept map were randomly assigned to the predefined fragments. Finally, the relations between the concepts and within the clusters were randomly drawn. Again, to raise readability of the inaccurate concept map, the relations were restricted to a range of between one relation and a maximum of three relations per concept (for an example, see Fig. 1c). These three safeguards (i.e., random number of fragments per explanation, random assignment of single concepts to fragments, random assignment of relations within the fragments) guaranteed that students received inaccurate feedback about their level and the allocation of cohesion gaps in their texts, as there were too many variables involved to generate accurate information coincidentally.</p> <hd id="AN0131619156-17">Procedure</hd> <p>The students were tested in small groups (n = 4) in our laboratory. Each student sat in front of a computer. Each seat was separated by a partition wall so that the students could neither see the computer screen of the others nor what kind of feedback the other students received. The students wrote their instructional explanations with the Microsoft Word© 2010 word processing program. The experiment lasted 60 min. At the beginning of the study, the students were informed that they would take part in a study on writing instructional explanations. After providing information about the scope of the study, we obtained oral consent from all individual participants to participate in the study. Then students filled out a short demographic questionnaire including the prior topic knowledge test. Afterwards, as in the study by Lachner et al. ([<reflink idref="bib16" id="ref65">16</reflink>] ), all the students received a short online tutorial about potential cohesion strategies (15 min). In particular, the main objective of the tutorial was to provide the students with conceptual knowledge about appropriate writing strategies for enhancing the cohesion of instructional explanatory texts (McNamara et al. [<reflink idref="bib23" id="ref66">23</reflink>] ). For that purpose, students first received a hypertext about the psychological mechanisms of text comprehension (i.e., the Construction-Integration-Model, see Graesser et al. [<reflink idref="bib8" id="ref67">8</reflink>] ). Second, students were informed about appropriate writing strategies for enhancing the cohesion of instructional explanatory texts (McNamara et al. [<reflink idref="bib23" id="ref68">23</reflink>] ).Then, the students were asked to provide an explanatory draft about cognitive load theory for a novice student (15 min). They were told that they should imagine a novice student having no scientific knowledge about cognitive load theory. Asking our students to have a specific audience in mind, when writing their instructional explanations, was used to establish the same reference point for the three experimental groups. Afterwards, students were randomly assigned to one of the three experimental conditions. After the students had finished their draft, the experimenter ran the local version of CohViz to generate the concept maps for the accurate concept map feedback condition and the inaccurate feedback condition. During the generation of the concept maps, the students played a complex memory game to distract them from the experimenter’s actions (10 min). Afterwards, all students were asked to revise their explanation. For that purpose, the students were prompted to pay attention to the cohesion of their instructional explanations and to resolve potential deficits with regard to the cohesion gaps by relating existing concepts to each other and adding potentially missing concepts. For their revision, the accurate concept map feedback group received a sheet with the accurate concept map of their draft and the prompt, whereas the inaccurate concept map feedback group received a sheet with the inaccurate concept map and the prompt. The no-feedback group only received a sheet with the prompt but without concept map. The students in the accurate concept map feedback and the inaccurate feedback conditions were additionally informed that they received a graphical representation of their own instructional explanation that visualized the cohesion gaps in their explanation. They were told that nodes represented the concepts of their explanation, arrows the logical and/or semantic relations between concepts, and isolated fragments of node-link-clusters (see Fig. 1) indicated cohesion gaps. The revision phase lasted 20 min. At the end of the experiment, the students were debriefed. Students received course credit for their participation. The experimenter followed the APA standards for ethical treatment of human participants.</p> <hd id="AN0131619156-18">Analysis and coding</hd> <hd id="AN0131619156-19">Level of cohesion</hd> <p>Following Lachner et al. ([<reflink idref="bib17" id="ref69">17</reflink>] ), we counted the number of cohesion gaps (i.e., the number of unrelated adjacent sentence pairs) to obtain a measure of cohesion. To this end, we first segmented the explanatory texts into sentence pairs. Afterwards, two independent raters coded for each sentence pair whether the sentence pair contained a cohesive tie that related the two sentences (e.g., a lexical reiteration, near-synonym, or a bridging information) or not (i.e., a cohesion gap). Interrater agreement was very good (κ = .94). For further analyses, we computed proportions of the cohesion gaps by dividing the number of cohesion gaps by the total number of adjacent sentences (McNamara et al. [<reflink idref="bib23" id="ref70">23</reflink>] ) to account for inter-individual differences with regard to text-length.</p> <hd id="AN0131619156-20">Results</hd> <p>We used an alpha level of.05 for all statistical analyses. As effect size measure, we used partial η<sups>2</sups> qualifying values &lt; .06 as small effect, values in the range between.06 and.14 as medium effect, and values &gt; .14 as large effect (see Cohen [<reflink idref="bib4" id="ref71">4</reflink>] ).</p> <p>A series of ANOVAs and χ<sups>2</sups> tests revealed no significant differences between the experimental conditions concerning age, F(<reflink idref="bib2" id="ref72">2</reflink>, 55) = 0.51, p = .61, gender, χ<sups>2</sups>(<reflink idref="bib2" id="ref73">2</reflink>) = 3.26, p = .20; number of enrolled semesters, F(<reflink idref="bib2" id="ref74">2</reflink>, 55) = 0.56, p = .57, and prior topic knowledge, F(<reflink idref="bib2" id="ref75">2</reflink>, 55) = 1.53, p = .23 (see Table 1). Furthermore, the students’ initial explanation drafts did not differ significantly between the experimental conditions in the total number of sentences, F(<reflink idref="bib2" id="ref76">2</reflink>, 55) = 1.31, p = .28, and the level of cohesion, as indicated by the number of cohesion gaps, F(<reflink idref="bib2" id="ref77">2</reflink>, 55) = 0.47, p = .96.</p> <p>Additional correlation analyses revealed that there were no significant relations between the students’ improvements of local cohesion and students’ prior topic knowledge, r(<reflink idref="bib57" id="ref78">57</reflink>) = − .14, p = .31, and between students’ improvements of local cohesion and the number of sentences of the draft, r(<reflink idref="bib57" id="ref79">57</reflink>) = .05, p = .69, indicating that students’ performance in the revised explanations was neither affected by their knowledge prerequisites, nor by the number of sentences of their drafts. Table 1 gives an overview of the descriptive statistics for the dependent variables.</p> <p>In order to test whether the accuracy of the concept map feedback contributed to students’ improvements of cohesion, we computed two contrast analyses, as suggested by Furr and Rosenthal ([<reflink idref="bib6" id="ref80">6</reflink>] ). First, to investigate whether the provision of feedback had an overall-effect on students’ improvements of cohesion, we first contrasted the accurate concept map condition and the inaccurate concept map condition with the no-feedback condition. Therefore, we assigned the following contrast weights to the experimental conditions: accurate concept map feedback = 0.5, inaccurate concept map feedback = 0.5, no feedback = − 1. In the contrast analysis, this contrast represented a fixed factor and the students’ improvement of cohesion was the dependent variable (i.e., the difference between the cohesion of the draft and the revised explanation). In line with our hypothesis, the contrast was significant, F(<reflink idref="bib1" id="ref81">1</reflink>, 55) = 6.39, p = .01, η<sups>2</sups> = .10 (medium effect), indicating that concept map feedback helped students improve the cohesion of their explanations more than no feedback.</p> <p>With the second contrast, we tested whether the accuracy of the concept map feedback additionally accounted for students’ improvements of cohesion. Thus, the students in the accurate feedback condition should improve the cohesion of their texts more than students with inaccurate concept map feedback. To test this hypothesis we assigned the following contrast weights to the experimental conditions: accurate concept map feedback = 1, inaccurate concept map feedback = − 1, no feedback = 0. Again, the contrast analysis confirmed our prediction: Students who received accurate concept map feedback improved the cohesion of their text to a more pronounced extent than students who had only inaccurate feedback at hand, F(<reflink idref="bib1" id="ref82">1</reflink>, 55) = 4.26, p = .02, η<sups>2</sups> = .07 (medium effect).</p> <p>Together, our findings indicate, that the accuracy of the concept map feedback contributed to students’ improvement of cohesion: Whereas students who received no feedback worsened their level of cohesion, students with accurate concept map feedback showed the highest improvements in cohesion from draft to revised explanations, and student who received inaccurate concept map feedback remained stable.</p> <hd id="AN0131619156-21">Discussion</hd> <p>Main goal of the present study was to experimentally examine the impact of the accuracy of computer-based feedback on students’ writing performance (i.e., improving explanatory texts for cohesion). Therefore, we used CohViz (Lachner et al. [<reflink idref="bib17" id="ref83">17</reflink>] ), a computer-based feedback tool which showed to have high agreements with human expert ratings, and either provided students with (a) accurate concept map feedback, (b) inaccurate concept map feedback, or (c) no feedback about the cohesion of their texts. Our findings provide evidence that the accuracy of the concept map feedback significantly impacted students’ writing, as students improved the cohesion of their instructional explanations most, when they were provided with accurate concept map feedback that provided an authentic conceptual structure of their explanations. In contrast, when students received inaccurate feedback, students could not further improve the cohesion of their texts, whereas without feedback, students even decreased the cohesion of their texts. Thus, we can conclude that the accuracy of computer-based feedback played a critical role for scaffolding students’ writing cohesive texts.</p> <p>For instance, Fig. 2 shows an excerpt of a student’s draft, the subsequent concept map and her revised explanation in the accurate concept map condition. The initial draft lacked cohesion, as it contained four unrelated fragments, which also impaired the overall comprehensibility of the draft. However, on the basis of the concept map feedback, the student was apparently able to partially close the depicted cohesion gaps. For instance, the student increased the cohesion of her explanation by inserting previously used concepts (i.e., “content”; learning), or by using connectives (i.e., “as”) which explicitly state the logic relationship between two sentences. These revisions particularly contributed to increase the cohesion of his text.Translated excerpt of a draft, the subsequent concept map, and the revised explanation by a student in the accurate concept map feedback condition. Bold words signify revisions not improving cohesion, whereas bold and underlined words signify revisions improving cohesion. Overall, cohesion gaps are marked by a vertical line in the text</p> <p>Besides the findings of the feedback accuracy, we could also replicate previous studies (Lachner et al. [<reflink idref="bib17" id="ref84">17</reflink>] ), as we found an overall effect of the concept map feedback on students’ writing. We attribute these beneficial effects particularly to the graphical re-visualization of the concept map feedback. Concept maps allow emphasizing critical textual features, such as explanatory cohesion, while other minor textual issues are ignored (e.g., grammar or punctuation).</p> <p>These graphical spatial representations can be regarded as advantageous for students revising their texts, as they visualize particular deficits of cohesion, and additionally provide specific information with regard to the allocation of such deficits. Hence, these effects may be comparable to effects of learning with multiple graphical representations (Ainsworth [<reflink idref="bib1" id="ref85">1</reflink>] ; Schnotz and Bannert [<reflink idref="bib36" id="ref86">36</reflink>] ). The concept map representation guides students’ search-processes, such as the search for cohesion gaps within a text, with rather low levels of investment of mental effort (Ainsworth, [<reflink idref="bib1" id="ref87">1</reflink>] ; Lachner et al. [<reflink idref="bib16" id="ref88">16</reflink>] ; Larkin and Simon [<reflink idref="bib19" id="ref89">19</reflink>] ). More importantly, the visualization of information in an additional representation may engage students to more deeply process and analyze the cohesion of their explanations in a self-regulated manner (Molloy and Boud [<reflink idref="bib24" id="ref90">24</reflink>] ), as such multiple representations require students to integrate the graphical representation into their sequential texts (Rau et al. [<reflink idref="bib31" id="ref91">31</reflink>] ).</p> <p>What are the broader implications of our study? From a theoretical perspective, our findings add to the scarce evidence of the effects of feedback accuracy on students’ writing. Previous studies on writing (e.g., Gielen et al. [<reflink idref="bib7" id="ref92">7</reflink>] ; Patchan et al. [<reflink idref="bib29" id="ref93">29</reflink>] ) mainly relied on correlational approaches, and analyzed relations between distinct textual feedback features and students’ writing performance. Although these studies provide valuable insights into the mechanisms of feedback, they do not allow drawing a causal link between the given feedback features, such as the accuracy, and students’ writing performance, as the feedback may have varied on a number of other dimensions, which could have impacted students’ writing performance. Thus, the current study is one of the first to provide causal evidence that the accuracy of computer-based feedback may largely impact students’ writing performance. As such, our study contributes to a better understanding of the instructional conditions that have to be met to provide effective computer-based feedback on students writing.</p> <p>From a practical perspective, our findings highlight the fact that the effectiveness of feedback systems largely depend on the deliberate design and implementation of such feedback systems. Particularly, for feedback systems on writing our findings are suggestive of ways that the particular feedback technology should be capable to validly capture distinct linguistic features, and provide accurate feedback with regard to distinct aspects of students’ writing. Given that there is a large variability among the recent computer-based feedback technologies, we think that more interdisciplinary research is needed which draws both on expertise in computer technology (e.g., natural language processing; machine learning) and on expertise in educational research, to fully exploit the potential of computer-based feedback technologies. Thus, both disciplines could bring their unique strengths to bear and be unified around the effective design of computer-based writing systems (Nathan et al. [<reflink idref="bib26" id="ref94">26</reflink>] ).</p> <hd id="AN0131619156-22">Study limitations and implications for further research</hd> <p>In our study, we sought to analyze effects of the accuracy of concept map feedback on students’ improvements of cohesion. Therefore, we compared an inaccurate feedback group which only received random feedback to an accurate feedback group, and a no-feedback group. This procedure allowed us to clearly disentangle effects of feedback accuracy on students’ writing. However, our study design was not sensitive for assessing the cognitive and affective effects which mediated our accuracy effect. From a cognitive perspective, the inaccuracy of feedback should have increased the students’ extraneous load while revising their feedback, as the students had to process the inaccurate feedback, and additionally make sense of the rather random representations during their revisions. Alternatively, from an affective perspective, the inaccurate feedback may have caused frustration in the students, which may have additionally impaired students’ revision performance. As such, future studies should try to disentangle the cognitive and affective effects of the provision of inaccurate feedback. For instance, process-analyses, such as think-aloud studies, could bring further insights into the underlying cognitive and affective processes which accounted for the effect of feedback accuracy.</p> <p>Beyond that, another limitation refers to the functionality of the CohViz tool, as it is only able to provide students with formative feedback about the level of cohesion of their expository texts. Although cohesion can be regarded as a critical text feature to enhance a reader’s comprehension of a text (e.g., Hall et al. [<reflink idref="bib10" id="ref95">10</reflink>] ; McNamara [<reflink idref="bib20" id="ref96">20</reflink>] ; Wittwer and Ihme [<reflink idref="bib41" id="ref97">41</reflink>] ), there are further critical textual features, such as the syntax or the concreteness of a text, that contribute to the comprehensibility of a text (McNamara [<reflink idref="bib20" id="ref98">20</reflink>] ). As such, future research should try to advance the functionality of CohViz, and integrate such additional text quality indicators, to provide students with accurate formative feedback to further enhance the quality of their texts.</p> <p>In conclusion, the present experiment shows that students can be successfully supported in writing cohesive explanatory texts—if they are provided with accurate graphical feedback. By this, accurate computer-based feedback can contribute to helping students acquire a crucial and indispensable skill in the information age: providing cohesive explanations.</p> <p>We would like to thank Christian Burkhart for the programming of the concept map feedback tool; Nathanael Kautz, and Tim Steininger for helping us with collecting and coding the data.</p> <hd id="AN0131619156-23">Compliance with ethical standards</hd> <hd id="AN0131619156-24">Conflict of interest</hd> <p>The authors declare that they have no conflict of interest.</p> <hd id="AN0131619156-25">Ethical statement</hd> <p>All procedures performed in this study were in accordance with the 1964 Helsinki declaration, and the German Psychological Society’s (DGPS) ethical guidelines. According to the DGPS guidelines, experimental studies only need approval from an institutional review board if participants are exposed to risks that are related to high emotional or physical stress or when participants are not informed about the goals and procedures included in the study. As none of these conditions applied to the current study, we did not seek approval from an institutional review board.</p> <hd id="AN0131619156-26">Informed consent</hd> <p>Informed consent was obtained from all individual participants included in the study.</p> <hd id="AN0131619156-27">References</hd> <hd id="AN0131619156-28">Citations</hd> <p>1 Ainsworth S, DeFT: a conceptual framework for considering learning with multiple representations, Learning and Instruction, 2006, 16, 3, 183, 198, 10.1016/j.learninstruc.2006.03.001</p> <ulist> <item>2 Berlanga AJ, Rosmalen P, Boshuizen HP, Sloep PB, Exploring formative feedback on textual assignments with the help of automatically created visual representations, Journal of Computer Assisted Learning, 2012, 28, 2, 146, 160, 10.1111/j.1365-2729.2011.00425.x</item> <item>3 Cho K, MacArthur C, Student revision with peer and expert reviewing, Learning and Instruction, 2010, 20, 4, 328, 338, 10.1016/j.learninstruc.2009.08.006</item> <item>4 Cohen J, Statistical power analysis for the behavioral sciences, 1988, 2, Hillsdale, NJ, Erlbaum</item> <item>5 Concha S, Paratore JR, Local coherence in persuasive writing: An exploration of Chilean students’ metalinguistic knowledge, writing process, and writing products, Written Communication, 2011, 28, 1, 34, 69, 10.1177/0741088310383383</item> <item>6 Furr RM, Rosenthal R, Evaluating theories efficiently: The nuts and bolts of contrast analysis, Understanding Statistics: Statistical Issues in Psychology, Education, and the Social Sciences, 2003, 2, 1, 33, 67, 10.1207/S15328031US0201_03</item> <item>7 Gielen S, Peeters E, Dochy F, Onghena P, Struyven K, Improving the effectiveness of peer feedback for learning, Learning and Instruction, 2010, 20, 4, 304, 315, 10.1016/j.learninstruc.2009.08.007</item> <item>8 Graesser AC, Millis KK, Zwaan RA, Discourse comprehension, Annual Review of Psychology, 1997, 48, 1, 163, 189, 10.1146/annurev.psych.48.1.163</item> <item>9 Graham S, Harris K, Hebert MA, Informing writing: The benefits of formative assessment. 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His research interest focuses on computer-based feedback on writing, and how cognitive components of expertise affect the design of (media-based) instruction.</p> <p>Iris Backfischis a research assistant at the Leibniz-Institut für Wissensmedien. Her research interest focuses on computer-based feedback on writing, and how teachers’ cognitive and motivational conditions affect their design of tablet-based instruction.</p> <p>Matthias Nücklesis a full professor of Educational Research at the University of Freiburg. Matthias Nückles’ research interests include computer-supported communication between experts and novices, the cognitive analysis of teaching competencies, and self-regulated learning.</p> </aug> <nolink nlid="nl1" bibid="bib27" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib34" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib20" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib8" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib22" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib41" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib23" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib11" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib28" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib10" firstref="ref11"></nolink> <nolink nlid="nl11" bibid="bib5" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib18" firstref="ref14"></nolink> <nolink nlid="nl13" bibid="bib9" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib12" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib24" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib3" firstref="ref22"></nolink> <nolink nlid="nl17" bibid="bib30" firstref="ref23"></nolink> <nolink nlid="nl18" bibid="bib38" firstref="ref24"></nolink> <nolink nlid="nl19" bibid="bib14" firstref="ref26"></nolink> <nolink nlid="nl20" bibid="bib32" firstref="ref27"></nolink> <nolink nlid="nl21" bibid="bib21" firstref="ref28"></nolink> <nolink nlid="nl22" bibid="bib33" firstref="ref29"></nolink> <nolink nlid="nl23" bibid="bib17" firstref="ref33"></nolink> <nolink nlid="nl24" bibid="bib15" firstref="ref34"></nolink> <nolink nlid="nl25" bibid="bib1" firstref="ref36"></nolink> <nolink nlid="nl26" bibid="bib25" firstref="ref37"></nolink> <nolink nlid="nl27" bibid="bib19" firstref="ref39"></nolink> <nolink nlid="nl28" bibid="bib2" firstref="ref40"></nolink> <nolink nlid="nl29" bibid="bib16" firstref="ref42"></nolink> <nolink nlid="nl30" bibid="bib37" firstref="ref45"></nolink> <nolink nlid="nl31" bibid="bib7" firstref="ref46"></nolink> <nolink nlid="nl32" bibid="bib13" firstref="ref47"></nolink> <nolink nlid="nl33" bibid="bib40" firstref="ref48"></nolink> <nolink nlid="nl34" bibid="bib29" firstref="ref50"></nolink> <nolink nlid="nl35" bibid="bib35" firstref="ref60"></nolink> <nolink nlid="nl36" bibid="bib39" firstref="ref61"></nolink> <nolink nlid="nl37" bibid="bib4" firstref="ref71"></nolink> <nolink nlid="nl38" bibid="bib57" firstref="ref78"></nolink> <nolink nlid="nl39" bibid="bib6" firstref="ref80"></nolink> <nolink nlid="nl40" bibid="bib36" firstref="ref86"></nolink> <nolink nlid="nl41" bibid="bib31" firstref="ref91"></nolink> <nolink nlid="nl42" bibid="bib26" firstref="ref94"></nolink> |
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| Items | – Name: Title Label: Title Group: Ti Data: Does the Accuracy Matter? Accurate Concept Map Feedback Helps Students Improve the Cohesion of Their Explanations – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lachner%2C+Andreas%22">Lachner, Andreas</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5866-7164">0000-0001-5866-7164</externalLink>)<br /><searchLink fieldCode="AR" term="%22Backfisch%2C+Iris%22">Backfisch, Iris</searchLink><br /><searchLink fieldCode="AR" term="%22Nückles%2C+Matthias%22">Nückles, Matthias</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Educational+Technology+Research+and+Development%22"><i>Educational Technology Research and Development</i></searchLink>. Oct 2018 66(5):1051-1067. – Name: Avail Label: Availability Group: Avail Data: Springer. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 17 – Name: DatePubCY Label: Publication Date Group: Date Data: 2018 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Concept+Mapping%22">Concept Mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+%28Composition%29%22">Writing (Composition)</searchLink><br /><searchLink fieldCode="DE" term="%22Connected+Discourse%22">Connected Discourse</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Evaluation%22">Writing Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Improvement%22">Writing Improvement</searchLink><br /><searchLink fieldCode="DE" term="%22Outcomes+of+Education%22">Outcomes of Education</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Instruction%22">Computer Assisted Instruction</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1007/s11423-018-9571-4 – Name: ISSN Label: ISSN Group: ISSN Data: 1042-1629 – Name: Abstract Label: Abstract Group: Ab Data: Students are often challenged by the demand of writing cohesive explanatory texts. Prior research has shown that providing students with concept map feedback that visualizes explanatory cohesion deficits helped students generate more cohesive explanations. We conducted an experiment to investigate whether the accuracy of the provided information within the concept map feedback affected students' improvements of cohesion. Accordingly, we varied the represented accuracy of information within such concept maps: Students either received accurate concept map feedback that depicted the real relations between concepts, as well as the authentic cohesion gaps in their explanations, or students received inaccurate concept map feedback, which depicted randomly drawn relations and random cohesion gaps. Additionally, in a baseline condition, students did not receive any feedback. We found that the students in the accurate feedback condition generated more cohesive explanations than the students in the no-feedback condition, whereas the students in the inaccurate feedback condition lay in-between. Evidently, providing feedback in general can be regarded as beneficial to enhance students' writing. However, the accuracy of the provided feedback further impacts the effectiveness of computer-generated concept maps. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: Ref Label: Number of References Group: RefInfo Data: 41 – Name: DateEntry Label: Entry Date Group: Date Data: 2018 – Name: AN Label: Accession Number Group: ID Data: EJ1190144 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11423-018-9571-4 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1051 Subjects: – SubjectFull: Accuracy Type: general – SubjectFull: Concept Mapping Type: general – SubjectFull: Feedback (Response) Type: general – SubjectFull: Writing (Composition) Type: general – SubjectFull: Connected Discourse Type: general – SubjectFull: Writing Evaluation Type: general – SubjectFull: Writing Improvement Type: general – SubjectFull: Outcomes of Education Type: general – SubjectFull: Computer Assisted Instruction Type: general Titles: – TitleFull: Does the Accuracy Matter? Accurate Concept Map Feedback Helps Students Improve the Cohesion of Their Explanations Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lachner, Andreas – PersonEntity: Name: NameFull: Backfisch, Iris – PersonEntity: Name: NameFull: Nückles, Matthias IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 1042-1629 Numbering: – Type: volume Value: 66 – Type: issue Value: 5 Titles: – TitleFull: Educational Technology Research and Development Type: main |
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