Cognition and Instruction
Situated Expertise in Literary Interpretation: An Expert-Expert Study of High School and PhD Students Reading Canonical Hip-Hop and Poetry
Youth Enacting Social-Spatial Justice in Middle School STEM: Advancing Justice Work in Hyperlocal and Interscalar Ways
Relationality and Ojibwemowin† in Forest Walks: Learning from Multimodal Interaction about Land and Language
Exploring the relationships among the dimensions of a community of inquiry in an online learning environment
Conservatory and music schoolteachers’ experiences with videoconferencing software during and after the COVID-19 pandemic
Does the early bird catch the worm? A large-scale examination of the effects of early participation in online learning
Learner-generated material: the effects of ubiquitous photography on foreign language speaking performance
The widespread availability of mobile phones has facilitated mobile learning and ubiquitous learning in language education. Although numerous benefits have been documented, the evidence for speaking fluency enhancement is relatively scant. Firmly grounded in humans’ cognitive structure and learners’ prior knowledge, this study proposes a ubiquitous photography strategy as a form of generative learning strategy. Specifically, besides the photos in English textbooks, foreign language (FL) learners at the college level were encouraged to use their mobile phones to capture photos to practice visual prompted oral tasks. Their learning experience was measured by a self-report questionnaire, triggering their perceptions of mental effort, task complexity, and learning preferences. Their learning outcome was measured by speech analysis of their oral performance, targeting fluency, and vocabulary diversity. Data analysis revealed that ubiquitous photography induced a better learning experience and enhanced their speaking outcomes to various extents. Results contribute to the potential of integrating ubiquitous learning and generative learning strategies in FL classrooms.
This experimental investigation considers how the inherent conceptual structure of external representations influences individuals' knowledge structure, and in addition proposes a measure of global collective knowledge to account for the influence of pre-existing knowledge structure. In two studies, undergraduates in a hospitality management course completed a pre-knowledge structure (pre KS) measure, a prior knowledge pretest, then read parallel versions of either a text or a table about the Internet of Things, then completed a post knowledge structure (post KS) measure, and finally completed a comprehension posttest. Analysis of the comprehension posttest data showed that the text group significantly outperformed the table group (p < .05) mainly due to performance on factual and main idea items, but not inference items. The pre- and post-KS data were analyzed as Pathfinder networks. Descriptive comparisons of between group networks (group–group) and within group networks (pre-post) showed that the table and text between-group networks were quite alike before reading and were even more alike after reading (i.e., peer convergence of local collective knowledge structure). The within-group network overlap from pre-to-post was also substantial. In addition, pre-to-post similarity with the expert shows the text group networks became more like the expert referent but the table group networks became less like the expert referent. Exploratory findings for this global collective knowledge network approach based on Google Ngram frequency dependencies were partially supported. For theory building, the results show how the influence of external representations can be framed in terms of a representation's inherent conceptual structure. For practice, this list-wise measure for eliciting knowledge structure provides a quick way to elicit individual and group-level knowledge structure networks that can be used in ordinary classrooms for formative and summative assessment.
The interaction effects of an instructor’s emotions in instructional videos and students’ emotional intelligence on L2 vocabulary learning
Language learning has long been a topic of interest, and instructional videos which allow students to learn anywhere and anytime have become an important language learning tool. However, the emotional characteristics of both instructors and students, which have the potential to influence students’ second language learning from instructional videos, have yet to be fully explored. The current study investigated the interaction effects of an instructor’s emotions (positive vs. negative vs. neutral) and students’ emotional intelligence (low vs. high) on students’ second language vocabulary learning from instructional videos with consideration of attention paid to the learning material (i.e., average fixation time, referring to the duration of each fixation on the learning material), learning experience (i.e., motivation, engagement, interaction), and learning performance (both immediate and delayed). Results showed that (1) only the interaction effect on attention was verified, and that (2) students with high emotional intelligence showed a larger average fixation time in the positive condition than in the negative condition, while (3) students with low emotional intelligence showed a smaller average fixation time in the neutral condition than in the negative condition. Furthermore, the results verified the benefits of the instructor’s positive emotion on students’ motivation, interaction, and immediate performance. Our findings shine a light onto the influence of an instructor’s emotions and students’ emotional intelligence on second language learning, and provide practical implications for the design of instructional videos and second language learning.
Development of the Design Thinking and Instructional Lessons (DTAIL) model: a creative approach for teachers
The educational landscape continues to become increasingly complex, which suggests a need for a teacher-driven creative approach to developing instructional lessons. This article introduces the Design Thinking and Instructional Lessons (DTAIL) model and describes its three-phase development. In Phase I, the Design Thinking literature and the first draft of the model are described. In Phase II and III, two design studies conducted with STEM K-12 public school and community college in-service teachers participating in summer research experience for teachers (RET) programs in the United States are described. In addition, during the second design study, ten teacher-participants were observed as they implemented their lessons and were interviewed concerning how and to what extent they perceived the DTAIL model to resonate with their approach to developing instructional lessons. Revisions to the model were made based on data analysis from those three design phases. Findings suggest that Design Thinking models that facilitate teacher-driven design of instructional lessons might usefully include design stages with an explicit depiction of rotation and recursiveness. In addition, Design Thinking models should also depict (1) iteration, reflection, and revision; (2) a chaotic fluctuating problem–solution space, and (3) circling backward to eventually narrow the problem space toward a satisficed solution. Furthermore, the majority of teacher-participants found the DTAIL model to resonate with their approach to developing instructional lessons.
We examine the effect of an innovation in an educational context, a class of 500 + first-year economics students at a well-known Australian university. We study whether introducing content in the form of a multimedia presentation has a detectable effect on specific categories of student knowledge. The multimedia presentation has a narrator presenting concepts with images, words, and worked examples. Our key outcome measure is the probability of answering questions correctly on a mid-term test. A quasi-experimental design is followed to offer a causal interpretation of the results. We find that the multimedia presentation markedly increases students’ academic outcomes on the test compared to those that did not view the presentation, especially in regards to procedural and evaluative knowledge. An additional survey reveals gains in students’ metacognitive knowledge. These findings suggest that multimedia presentations contribute to improved student learning outcomes and offer valuable options at a time of increased online course delivery. The findings also highlight the relevance of investing in education and resources to develop the necessary design skills among academics and staff.
With the challenges of a global pandemic, political and social unrest, and the consequences these issues bring, there is a universal call for empathy as we attempt to maneuver through this tumultuous time. For instructional designers, this includes employing empathy and empathic design as they grapple with how to design instructional interventions for learners. Empathy is the first stage in the design thinking process, now a popular buzz word in design research and practice. It suggests that empathy results in a design that meets the audience needs. But how do we know if this is true? As professors of instructional design and researchers of design practice, we teach empathy for action as a means for design students to act by producing a meaningful design deliverable. Over a 15-week semester, we taught and measured designer empathy and empathic design with 31 graduate students while they worked in design teams, participating in authentic design projects with two nonprofit organizations. Results indicate that 75% of the instances of empathy were students showing sensitivity to the end-learners’ experiences and situations, 52% were directed toward identifying with the end-learners’ thoughts and feelings. This did not necessarily translate to the designed deliverables as only three of the nine student teams created final meaningful design deliverables. We report on our instructional process, our research results and provide the framework for what we believe is needed to bridge the connection of empathy, empathic design, and meaningful design deliverables.
Does slow and steady win the race?: Clustering patterns of students’ behaviors in an interactive online mathematics game
Online educational games have been widely used to support students’ mathematics learning. However, their effects largely depend on student-related factors, the most prominent being their behavioral characteristics as they play the games. In this study, we applied a set of learning analytics methods (k-means clustering, data visualization) to clickstream data from an interactive online algebra game to unpack how middle-school students’ (N = 227) behavioral patterns (i.e., the number of problems completed, resetting problems, reattempting problems, pause time before first actions) correlated with their understanding of mathematical equivalence. The k-means cluster analysis identified four groups of students based on their behavioral patterns in the game: fast progressors, intermediate progressors, slow progressors, and slow-steady progressors. The results indicated that students in these clusters, with the exception of slow progressors, showed significant increases in their understanding of mathematical equivalence. In particular, slow-steady progressors, who reattempted the same problem more often than other students, showed the largest absolute learning gains, suggesting that behavioral engagement played a significant role in learning. With data visualizations, we presented evidence of variability in students’ approaches to problem solving in the game, providing future directions for investigating how differences in student behaviors impact learning.
Connecting learning and playing: the effects of in-game cognitive supports on the development and transfer of computational thinking skills
Prior studies on game-based learning provide limited and mixed results in the transfer of skills learned during game play to contexts outside of the game. This study tested the effects of playing a blocked-based programming educational game implemented with in-game cognitive supports on students’ ability to learn and apply computational thinking (CT) skills in near and far transfer tasks. With 79 students randomly assigned to one of two conditions, the control group received basic game supports and the treatment group received cognitive supports in addition to the basic game supports. After two hours of total gameplay over the course of four days, both groups performed equally well, and students’ CT skills were improved significantly at the near transfer level but not at the far transfer level. Students in the control condition performed significantly better on far transfer compared to the students in the treatment condition. Regression analyses indicated that the overall use of the cognitive supports was infrequent, but the amount of time spent voluntarily using cognitive supports with help on goal setting and worked examples predicted far transfer performance. How students use the cognitive supports (subverting the use of cognitive support to conscientiously learn the computational skill by using them more as game cheat sheets) might explain these findings. Design implications and directions for future research on facilitating learning transfer with in-game supports are discussed.
Impacts of an AI-based chabot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation
Review strategies after learning new knowledge are essential for students to consolidate the key points, understand the subject content, analyze aspects of the learning topics, and summarize the knowledge content of learning while mastering new knowledge. However, educators have found that students generally have difficulties seeking help when they encounter learning problems. This could significantly affect their after-class review performances. To cope with this problem, an after-class review approach with an AI (Artificial Intelligence)-based chatbot is proposed in this study to provide students with immediate and quality feedback during the learning process. Moreover, a quasi-experiment was conducted to explore students’ learning motivation, attitude, and academic performance when using the AI-based chatbot. Participants were two classes of students from a university in Taiwan. One class with 18 students was the experimental group and the other with 20 students was the control group. The experimental group used the AI-based chatbot in the after-class review, while the control group used the conventional after-class review approach. Research results showed that the application of AI-based chatbots in the review process of public health courses could improve students’ academic performance, self-efficacy, learning attitude, and motivation. In other words, chatbots could help students become more active in the learning process. It is noted that after students asked questions, providing them with sufficient feedback during the review process could make them feel recognized and help to establish a relaxing and friendly interaction, thereby improving their academic performance.
A concept mapping-based prediction-observation-explanation approach to promoting students’ flipped learning achievements and perceptions
Previous research has illustrated the potential of flipped learning for assisting teachers in designing meaningful activities to promote students’ higher order thinking skills; however, several previous studies have challenged the effects of flipped learning on students’ learning. One of the key problems is the lack of an effective learning approach or tools to engage students in the flipped learning activity. In this study, a concept mapping-based Prediction-Observation-Explanation (POE) approach was incorporated into flipped learning (called CPOE-FL) to enhance students’ scientific learning. Furthermore, a three-group experiment was conducted to assess the effects of the three flipped learning models, comprising the CPOE-FL approach, the POE-FL (incorporating POE into flipped learning) approach, and the C-FL (conventional flipped learning) approach. The experimental results displayed that the CPOE-FL approach can benefit the learning achievements and self-efficacy of the students with respectively lower prior knowledge and lower initial self-efficacy, in comparison with the POE-FL and C-FL approaches. Both the CPOE-FL and POE-FL approaches promoted the students’ inner learning motivation, while the CPOE-FL approach enhanced the students’ critical thinking. This proposed approach could provide a good reference for researchers or school teachers intending to implement POE-based flipped learning in the future.
Explicit instruction in the context of whole-tasks: the effectiveness of the task-centered instructional strategy in computer science education
Novice programmers, who have yet to form effective mental models of the domain, often experience high cognitive load, low confidence, and high anxiety, negatively affecting learning and retention rates. These cognitive and affective limitations pose an instructional challenge. This study aimed to investigate the effectiveness of a whole-task instructional approach compared with a part-task instructional approach for novices learning to program from a cognitive and affective perspective. A fully randomized between-subjects controlled experiment was designed, including two online instructional conditions (whole-task vs. part-task). The whole-task condition followed the Task-Centered Instructional Strategy and included explicit instruction in the context of whole tasks. The part-task condition followed a part-task instructional strategy and included the same explicit instruction, yet in the context of objectives and topic-related tasks. Based on Bandura’s triadic model (Bandura, Social foundations of thought and action: A social cognitive theory, Prentice-Hall, 1986), we propose a conceptual model, which we used to hypothesize that the Task-Centered Instructional Strategy may be more effective for novices learning to program. Sixty-five students with no programming experience volunteered to participate in the study and were randomly assigned to one of the conditions. Participants in the whole-task condition performed significantly better on the near and far transfer posttests. In accordance with our model, confidence and cognitive load during learning were found to be significant partial mediators of the effect of instructional strategy on performance. Overall, we found that the task-centered instructional strategy, combining explicit instruction with whole-tasks, is effective for addressing the cognitive and affective considerations relevant to novices in computer science education.
Teacher emotions could make a difference to the development of technological pedagogical content knowledge (TPACK), a complicated knowledge essential for effective teaching with technology. Both experienced and novice teachers reported having experienced a series of emotional challenges as they acquire technology integration skills. Self-regulated learning (SRL), a series of cognitive and metacognitive learning processes in problem-solving, is associated with learners’ emotions as well. In this paper, we examine the influence of teaching experience and SRL on teachers’ emotions in the context of TPACK development. Particularly, we identify two distinct groups of teachers based on the extent to which they experience positive and negative emotional experience in the task using the clustering analysis method. Binary logistic regression was applied to test whether the model of teaching experience and SRL can predict previous emotion groups. Although the overall model was significant, only SRL was a significant individual predictor in this context. Regression analysis revealed a positive association between SRL and teacher emotions. We used a qualitative method to analyze teachers’ think-aloud protocols to further determine teaching experience and SRL’s influence on teacher emotions. The results supported previous findings that SRL can positively predict teachers’ emotions during the TPACK development task. Implications were discussed for providing emotional support to teachers during TPACK development.
Design and development of an online formative peer assessment environment with instructional scaffolds
Although formative peer assessment (FPA) has become a prevailing learning activity in different educational settings, there are not enough suggestions on how to structure it with instructional supports in online environments to optimize its benefits. Therefore, this study aims to propose design guidelines for the development of an effective online FPA environment with instructional scaffolds in the context of writing activities at high schools. To this end, an online FPA environment was designed on the basis of an assessment model for regulated learning and teachers’ and students’ experiences. It was evaluated and refined three times. The formative evaluation findings suggested designing an online FPA environment with preparatory activities, information resources, goal setting and planning, anonymity, criteria form, sustainable and supportive dialog, motivational elements, and an easy-to-use interface. As a result, 11 design guidelines were produced. Overall, this research provides a better understanding of the essential design elements of online FPA environments.
A web design course has complex and diverse skills, which may attract students with an interest in technology and art fields to learn to program. It makes a need to have a flexible learning framework to develop all students to learn in a programming course. This study was designed to develop students’ learning achievement and computational thinking (CT) abilities by using a Design Thinking (DT)—Conceive-Design-Implement-Operate (CDIO) engineering design framework in a flipped web programming course. The participants were 41 students (males = 17, females = 24) coming from a Taiwan University. All of the students (20–21 years old) had e-learning-related backgrounds in a teacher’s college. The experiment was conducted for 14 weeks. The flipped learning and flipped DT-CDIO course each had a total teaching time of 6 weeks, and the midterm exam and final exam each took one week. We used a questionnaire and formative assessment to examine the students’ computational thinking ability and learning achievement before and after the course was applied. The results showed the students significantly improved their learning achievement and computational thinking ability. There were no significant gender differences in learning achievement. Some gender differences could be seen in some dimensions of CT ability. This study shows that the DT-CDIO framework brings many benefits to promote interdisciplinary learning by attracting STEAM talent and providing evidence to support the importance of flipped web programming courses.
The rich multimedia-enhanced language content offered by modern commercial off-the-shelf games and students’ interest in playing such games has motivated efforts for seeking effective means to integrate them into the curriculum to enrich and enhance foreign language learning. Despite the general interest and appeal of game-enhanced learning in foreign language learning, there is a need for strategies for effective curriculum integration and empirical studies to test the effects of such interventions systematically. This study aims to contribute to this need by investigating the effectiveness of a ten-week-long game-enhanced language learning intervention on English foreign language learning. The study employed an embedded mixed methods design, including a controlled experiment and semi-structured interviews. The experiment group (n = 38) participated in a game-enhanced language learning program that was designed based on the Play Curricular activity Reflection Discussion (PCaRD) framework, whereas the control group (n = 38) received conventional instruction. Students took the TOEFL-ITP and L2 motivational self-system questionnaire before and after the intervention, whereas qualitative data were gathered via semi-structured interviews. The results indicated that both groups had significantly improved their scores, yet no significant differences were found in their post-test scores. The motivation questionnaire revealed a significant difference in cultural interest and attitudes to target community dimensions in favor of the game-enhanced condition. Moreover, the interview results indicated that participants had positive attitudes towards integrating commercial games into their language classrooms. Although the experimental group did not significantly outperform the control group, the game-enhanced intervention provided an equally effective learning experience with improved motivational attributes.
Immersive virtual reality in STEM: is IVR an effective learning medium and does adding self-explanation after a lesson improve learning outcomes?
The goal of the current study was to investigate the effects of an immersive virtual reality (IVR) science simulation on learning in a higher educational setting, and to assess whether using self-explanation has benefits for knowledge gain. A sample of 79 undergraduate biology students (40 females, 37 males, 2 non-binary) learned about next-generation sequencing using an IVR simulation that lasted approximately 45 min. Students were randomly assigned to one of two instructional conditions: self-explanation (n = 41) or control (n = 38). The self-explanation group engaged in a 10 min written self-explanation task after the IVR biology lesson, while the control group rested. The results revealed that the IVR simulation led to a significant increase in knowledge from the pre- to post-test (ßPosterior = 3.29). There were no differences between the self-explanation and control groups on knowledge gain, procedural, or conceptual transfer. Finally, the results indicate that the self-explanation group reported significantly higher intrinsic cognitive load (ßPosterior = .35), and extraneous cognitive load (ßPosterior = .37), and significantly lower germane load (ßPosterior = − .38) than the control group. The results suggest that the IVR lesson was effective for learning, but adding a written self-explanation task did not increase learning after a long IVR lesson.
Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing.
Elementary Students Learning Computer Programming: an investigation of their knowledge Retention, Motivation, and perceptions
Students need to learn and practice computational thinking and skills throughout PreK-12 to be better prepared for entering college and future careers. We designed a math-infused computer science course for third to fifth graders to learn programming. This study aims to investigate the impact of the course on students’ knowledge acquisition of mathematical and computational concepts, motivation, and perceptions of the computing activities. Fifty-one students at a Boys and Girls Club participated in the study. Data collection procedures include pre- and post-tests, pre- and post-surveys, in-class observations, and one-on-one interviews. Results indicate that students have improved significantly on mathematical and computational concepts. They also tended to believe computer programming is fun, comprehensible, enjoyable, and were able to perceive the value of learning it. Implications and recommendations for future research are also discussed.
Examining teachers’ behavior patterns in and perceptions of using teacher dashboards for facilitating guidance in CSCL
Learning analytics dashboards have been developed to facilitate teacher guidance in computer-supported collaborative learning (CSCL). As yet, little is known about how teachers interpret dashboard information to facilitate guidance in their teaching practice. This study examined teachers’ behavior patterns in interpreting information from dashboards, and obtained their views about the potential barriers in interpreting dashboard information. Fourteen pre-service teachers participated in the study and data were collected from multiple sources. In total, 1,346 min of video data on teachers’ guiding behavior and approximately 27,000 words from a cued retrospective report and interview data were generated. A two-stage approach was adopted to process the data. Based on the video analysis in the first stage, we extracted teachers’ four typical behavior patterns in finding and reading dashboard information and two behavior patterns when explaining information from dashboards. Thematic analysis at the second stage identified useful indicators for teacher guidance in CSCL and some major barriers teachers encountered in interpreting information. These findings may help improve the design of dashboards and show how teachers integrate dashboards into their daily teaching practice, thereby enhancing students’ collaboration and learning.
Did library learners benefit from m-learning strategies? Research-based evidence from a co-citation network analysis of the literature
Mobile learning strategies have been employed for social learning activities, including library- and museum-supported learning. Previous studies have reviewed the literature from the technological aspect. However, a retrospective study from the perspective of bibliometric and network structure has not yet been provided. The aim of this study was therefore to systematically review journal papers on library-supported mobile learning (LibML). A coding framework including library types, mobile learning strategies, and research issues was adopted based on the literature and was used to screen and categorize the research papers. A co-citation network analysis was then adopted to analyze and visualize the structural relationships among the papers. A total of 53 eligible articles with 1370 citations in follow-up studies were collected from the Scopus database. The results showed that two main research streams of LibML were identified from the overall network structure, including library- and museum-supported mobile learning. In terms of the mobile learning strategy, library-supported research mainly focused on self-directed learning, whereas museum-supported research emphasized inquiry-based learning. In terms of research issues, most library-supported research focused on patrons’ affective engagement, whereas museum-supported research emphasized learning performance. This study provides a citation-based approach to reveal the research trends and mainstream LibML research. The main contribution of combining co-citation and social network analysis is to provide a visualized network diagram of LibML research. Limitations of the methodological approach are noted. Discussion and future directions from the follow-up study are provided.
How to assist the students while learning from text? Effects of inserting adjunct questions on text processing
This study analyzes the effect of text-inserted questions and post-text-reading questions, i.e., questions timing, on students’ processing and learning when studying challenging texts. Seventy-six freshmen read two science texts and answered ten adjunct questions with the text available, being tested on learning 5 days afterwards. Questions were presented either after reading the whole text or inserted in the text after reading the relevant information. Online processing data were recorded while reading and searching the texts, and measures of processing strategies (i.e., paraphrases, elaborations) while answering the questions were collected. Compared to students in the post-reading condition, those in the inserted condition spent more time reading the text initially, were more efficient at searching for information in the text, and produced more accurate elaborations, all of which may explain why answering inserted questions in an available text were more effective in terms of learning than answering post-reading questions. Limitations and educational implications of these results are also discussed.
Localizing, describing, interpreting: effects of different audio text structures on attributing meaning to digital pictures
Based on previous research on multimedia learning and text comprehension, an eye-tracking study was conducted to examine the influence of audio text coherence on visual attention and memory in a multimedia learning situation with a focus on picture comprehension. Audio text coherence was manipulated by the type of LDI structure, that is, whether localization, description, and interpretation followed in immediate succession for each pictorial detail or whether localizations and description of details were separated from their interpretation. Results show that with a LDI integrated structure compared to a LDI separated structure the referred-to picture elements were fixated longer during interpretation parts, and linkages between descriptions and interpretations were better recalled and recognized. The effects on recall and recognition of linkages were fully mediated by fixation times. This pattern of results can be explained by an interplay between audio text coherence and dual coding processes. It points out the importance of local coherence and the provision of localization information in audio explanations as well as visual attention to allow for dual coding processes that can be used to better attribute meaning to picture details. Practical implications for the design of educational videos, audio texts on websites, and audio guides are discussed.
Investigating factors affecting student academic achievement in mathematics and science: cognitive style, self-regulated learning and working memory
Studies indicate that learners’ cognitive style (CS), self-regulated learning (SRL), and working memory (WM) are associated with their academic performance. These studies describe the relationship of academic achievement with SRL, CS, or WM individually or pairwise relationships between SRL, CS, and WM rather than the overall relationship between academic achievement and each factor. In this study, a structural equation modelling (SEM) analysis was conducted to explore the overall theoretical relationship. We focused on academic achievements in mathematics and science (AAMS). A total of 191 sixth-grade students (male: 111, female: 80; mean age: 11.08 years, SD = 0.282) from two public elementary schools in Taiwan was selected as valid samples for this study. The findings indicated that CS, WM, and SRL individually had significant influences on AAMS, among which SRL had the largest effect, followed by WM and CS. Furthermore, we discovered that CS was significantly correlated with WM. The results of the analysis of the mediation effect demonstrated that CS both directly affected AAMS and indirectly affected AAMS through SRL. The implication of the findings and recommendations are also discussed.
Models and modeling are central to both scientific literacy and practices as demonstrated by the Next Generation Science Standards. Through a design-based research framework, we developed a model-based assessment (MBA) and associated rubric as tools for teachers to understand and support students in their conceptualization of the flow of energy and matter within ecosystems. The MBA was piloted with four middle school students (n = 4) and implemented in two sixth grade student cohorts (n = 89 & n = 98). The MBA and rubrics went through several design iterations in order to best capture student understanding of complex systems. The design of the MBA allows students to express conceptual understanding while also capturing the transformation of their understanding as they are exposed to new information and experiences within the curricular content.
Our objective in this study was to investigate how the eye-movement behavior and concurrent verbal protocols of students with high-/low-prior-knowledge were reflected in the use of multiple representations for scientific argumentation. We also examined the degree of consistency between eye-fixation data and verbalization to ascertain how and when the eye-mind hypothesis (EMH) applies in this subdomain of scientific argumentation. Our results focused on fixation duration and recorded arguments from 96 college students. The high-prior-knowledge group did not present static patterns in the inspection of multiple representations, which indicates that they tended to select representations according to the contingent demands of the current task, indicating that for them, there was no “most appropriate representation”. The high-prior-knowledge group also submitted a greater number of representations and more frequently mentioned multiple representations in their verbal protocols. Finally, the students demonstrated notable discrepancies between eye-movement data and verbal protocols related to representations as well as inconsistencies with previous findings. Thus, the fact that the EMH does not always hold could perhaps be attributed to the scope of interpretation in argumentation tasks and the complexity of information related to some representations, both of which could hinder the instantaneous formation of a gist. Our findings may contribute to reducing the ambiguity and uncertainty involved in the analysis of eye-fixation data when multiple representations are employed for scientific argumentation.
The collaborative discourse characteristics of high school students during a web-based module for a socioscientific issue
In order to cultivate students to be able to participate in public affairs and make decisions about socioscientific issues (SSI), a web-based module was designed for students to collaboratively engage in the decision-making (DM) process. This study attempted to identify students’ discourse characteristics that might lead to formulating an evidence-based decision on SSI. Twenty-nine Grade 10 students were randomly divided into eight groups of three or four. The transcribed data of one case from each performance level were compared to investigate the interplay between groups’ DM performances and discourse characteristics. The results showed that the group that gained a high score on the DM group worksheet engaged in the metacognitive discussion for planning procedures of the module tools and in the conceptual exchanges to accomplish the tasks. The members of this group could initiate and extend ideas, provide prompts, and confirm or reject each other’s ideas, resulting in sustained interactive dialogs that allowed them to learn from one another. This indicated that students need to be encouraged to clarify the task goals, plan procedures, monitor their performance, and exchange their ideas actively. The implications of how collaborative discourse promote students’ SSI DM performance, and the better design and enactment of SSI modules are discussed.
Computational thinking (CT) and computer science (CS) are becoming more widely adopted in K-12 education. However, there is a lack of focus on CT and CS access for children with disabilities. This study investigates the effect of the robot development process at the secondary school level on the algorithmic thinking and mental rotation skills of students with learning disabilities (LD). The study was conducted with the single-subject model and as an A-B-A design. In the study, the CT skill development of four students with LD (1 female, 3 male) was monitored throughout 13 weeks with the pre-treatment sessions running from weeks 1–4, treatment sessions running from weeks 5–9, and post-treatment sessions running from weeks 10–13. During the treatment sessions, robot design and programming implementations were performed. During these 13 sessions, the observer scored participants’ both algorithmic problem-solving and mental rotation skills. These skills are also required to use some other cognitive sub-skills (i.e., selective attention, processing speed) which were defined by ten special education experts at the beginning of the study. All these skills were evaluated according to how well the students performed the following four criteria: (1) To start to perform the instructions quickly (processing speed), (2) to focus on the task by filtering out distractions (selective attention), (3) to fulfill the task without having to have the instructions repeated, (4) to perform algorithmic problem-solving/mental rotation tasks without any help. Considering the results on the participants’ algorithmic problem-solving skills, a significant improvement was obtained in their skills after the treatment process. The improvement obtained in the participants’ mental rotation skills is another important result of the study. Considering the study results from a holistic perspective, it can be concluded that the robot development implementation, as educational technology, can be used to support the cognitive development of students with learning disabilities.
Using heuristic worked examples to promote solving of reality-based tasks in mathematics in lower secondary school
This study examined whether learning with heuristic worked examples can improve students’ competency in solving reality-based tasks in mathematics (mathematical modeling competency). We randomly assigned 134 students in Grade 5 and 180 students in Grade 7 to one of three conditions: control condition (students worked on reality-based tasks), worked example condition (students studied worked examples representing a realistic process of problem-solving by fictitious students negotiating solutions to the tasks), and prompted worked example condition (students additionally received self-explanation prompts). In all three conditions, the students worked on the tasks individually and independently for 45 min. Dependent measures were mathematical modeling competency (number of adequate solution steps and strategies) and modeling-specific strategy knowledge. Results showed that although strategy knowledge could be improved through the intervention for fifth and seventh graders, modeling competency was improved only for seventh graders. The prompting of self-explanations had no additional effect for either fifth or seventh graders.
Concept map (CM) is introduced as a useful tool for studying students’ system thinking (ST). However, it is more known to represent students’ knowledge of system components and organization and less recognized as a tool to examine and enhance students’ understanding about the underlying causal mechanisms in complex systems. In this study, through a mixed method approach, we investigated the potential of CM in demonstrating undergraduate students’ (n = 173) ST. We also conducted a comparative analysis to examine the effects of different scaffolding on developing students’ ST skills. Through a theoretical framework of causal patterns, we present a new perspective on what CM reveals about students’ ST and what are its limitations in showing system complexities. The results indicated that CM can provide a platform for students to practice causal mechanisms such as domino, mutual, relational, and cyclic causalities, and accordingly, work as a tool for teachers to examine students’ knowledge of such mechanisms. The results also showed that students improved in demonstrating ST by CM when they were scaffolded for showing causal mechanisms and building CM. Eventually, this study concludes that the CM is a highly relevant tool to increase and examine students’ ST skills. To this end, we found it is important to explicitly teach students about causal patterns and guide them to construct CM with an emphasis on showing the interconnection among concepts.
We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.
Instructional videos have been widely used in online learning environments. Effective video learning requires self-regulation by learners, which can be facilitated by deliberate instructional design, such as through prompting. Grounded in the interactive, constructive, active, and passive (ICAP) framework, this study compared the effects of explanation prompts and explored how they affected the retention and transfer of learning. In an online experiment, 103 participants were randomly assigned to focused self-explanation, scaffolded self-explanation, and instructional explanation prompting conditions. The results indicated better retention performance from the scaffolded prompt than from the focused prompt. No differences were found in transfer performance across various forms of prompts. Regression analysis suggested that prior knowledge and cognitive load may have interacted with the effect of self-explanation prompts. Prior knowledge positively predicted transfer performance, and cognitive load negatively predicted transfer performance when focused or scaffolded prompts were implemented. Potential explanations concerning how self-explanation prompts affect learning were discussed.
Futurising science education: students’ experiences from a course on futures thinking and quantum computing
To promote students’ value-based agency, responsible science and sustainability, science education must address how students think about their personal and collective futures. However, research has shown that young people find it difficult to fully relate to the future and its possibilities, and few studies have focused on the potential of science education to foster futures thinking and agency. We report on a project that further explored this potential by developing future-oriented science courses drawing on the field of futures studies. Phenomenographic analysis was used on interview data to see what changes upper-secondary school students saw in their futures perceptions and agentic orientations after attending a course which adapted futures thinking skills in the context of quantum computing and technological approaches to global problems. The results show students perceiving the future and technological development as more positive but also more unpredictable, seeing their possibilities for agency as clearer and more promising (especially by identifying with their peers or aspired career paths), and feeling a deeper connection to the otherwise vague idea of futures. Students also felt they had learned to question deterministic thinking and to think more creatively about their own lives as well as technological and non-technological solutions to global problems. Both quantum physics and futures thinking opened new perspectives on uncertainty and probabilistic thinking. Our results provide further validation for a future-oriented approach to science education, and highlight essential synergies between futures thinking skills, agency, and authentic socio-scientific issues in developing science education for the current age.
Figuring out what works: learning and engaging with ideas about evolution within integrated informal learning environments
Informal learning environments can be a fun and effective means of introducing visitors to a variety of topics in evolution. Our study examined 120 sixth-grade students’ conceptualisation of evolutionary ideas following three evolution-themed “Science Days” at ‘Nature Campus’—an informal learning environment in Central Israel comprised of a natural history museum, zoological and botanical gardens. The students visited Nature Campus in groups of twenty. After each science day, the students worked in teams of 4–5 to make a poster, based on five pictures representing topics from the learning environment. This poster-making process served as a knowledge integration activity, aimed at assisting students in organizing all the knowledge from each science day, and integrating it with knowledge from the previous science days. Observations of students’ discussions while making their posters and video recordings of the activities throughout the science days were used as a basis for conclusions regarding which events in the program were recalled as meaningful by the students. The ideas and concepts that arose during the students’ poster making process demonstrated knowledge drawn from multiple activities in which they had engaged on Nature Campus, reflecting an understanding of evolution-related concepts from the fields of paleontology and ecology. Our findings showed that concepts and ideas that were taught via hands-on, interactive, inquiry-based learning in an authentic environment were later featured most prominently in the students’ poster-making discussions.
How many words are enough? Investigating the effect of different configurations of a software scaffold for formulating scientific hypotheses in inquiry-oriented contexts
We extended research on scaffolds for formulating scientific hypotheses, namely the Hypothesis Scratchpad (HS), in the domain of relative density. The sample comprised of secondary school students who used three different configurations of the HS: Fully structured, containing all words needed to formulate a hypothesis in the domain of the study; partially structured, containing some words; unstructured, containing no words. We used a design with two different measures of student ability to formulate hypotheses (targeted skill): A global, domain-independent measure, and a domain-specific measure. Students used the HS in an intervention context, and then, in a novel context, addressing a transfer task. The fully and partially structured versions of the HS improved the global measure of the targeted skill, while the unstructured version, and to a lesser extent, the partially structured version, favored student performance as assessed by the domain-specific measure. The partially structured solution revealed strengths for both measures of the targeted skill (global and domain-specific), which may be attributed to its resemblance to completion problems (partially worked examples). The unstructured version of the HS seems to have promoted schema construction for students who revealed an improvement of advanced cognitive processes (thinking critically and creatively). We suggest that a comprehensive assessment of scaffolding student work when formulating hypotheses should incorporate both global and domain-specific measures and it should also involve transfer tasks.
How preparation-for-learning with a worked versus an open inventing problem affect subsequent learning processes in pre-service teachers
A worked-out or an open inventing problem with contrasting cases can prepare learners for learning from subsequent instruction differently regarding motivation and cognition. In addition, such activities potentially initiate different learning processes during the subsequent (“future”) learning phase. In this experiment (N = 45 pre-service teachers), we aimed to replicate effects of earlier studies on learning outcomes and, on this basis, to analyze respective learning processes during the future-learning phase via think-aloud protocols. The inventing group invented criteria to assess learning strategies in learning journals while the worked-example group studied the same problem in a solved version. Afterwards, the pre-service teachers thought aloud during learning in a computer-based learning environment. We did not find substantial motivational differences (interest, self-efficacy), but the worked-example group clearly outperformed their counterparts in transfer (BF+0 > 313). We found moderate evidence for the hypothesis that their learning processes during the subsequent learning phase was deepened: the example group showed more elaborative processes, more spontaneous application of the canonical, but also of sub-optimal solutions than the inventing group (BFs around 4), and it tended to focus more on the most relevant learning contents. Explorative analyses suggest that applying canonical solutions to examples is one of the processes explaining why working through the solution leads to higher transfer. In conclusion, a worked-out inventing problem seems to prepare future learning more effectively than an open inventing activity by deepening and focusing subsequent learning processes.
Learning to solve ill-defined statistics problems: does self-explanation quality mediate the worked example effect?
Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for learning from examples suggests that self-explanation quality mediates effects of examples on learning outcomes (Atkinson et al. in Rev Educ Res 70:181–214, 2000). We investigated self-explanation quality as mediator in a worked examples—problem-solving paradigm. We developed a coding scheme to assess self-explanation quality in the context of ill-defined statistics problems and analyzed self-explanation data of a study by Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016). Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016) investigated whether the worked example effect depends on prior knowledge, working memory capacity, shifting ability, and fluid intelligence. In our study, we included these variables to jointly explore mediating and moderating factors when individuals learn with worked examples versus through problem-solving. Seventy-four university students (mean age = 23.83, SD = 5.78) completed an open item pretest, self-explained while either studying worked examples or solving problems, and then completed a post-test. We used conditional process analysis to test whether the effect of worked examples on learning gains is mediated by self-explanation quality and whether any effect in the mediation model depends on the suggested moderators. We reproduced the interaction effects reported by Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016) but did not detect a mediation effect. This might indicate that worked examples are directly effective because they convey a solution strategy, which might be particularly important when learning to solve problems that have no algorithmic solution procedure.
Detecting threshold concepts through Bayesian knowledge tracing: examining research skill development in biological sciences at the doctoral level
Threshold concepts are transformative elements of domain knowledge that enable those who attain them to engage domain tasks in a more sophisticated way. Existing research tends to focus on the identification of threshold concepts within undergraduate curricula as challenging concepts that prevent attainment of subsequent content until mastered. Recently, threshold concepts have likewise become a research focus at the level of doctoral studies. However, such research faces several limitations. First, the generalizability of findings in past research has been limited due to the relatively small numbers of participants in available studies. Second, it is not clear which specific skills are contingent upon mastery of identified threshold concepts, making it difficult to identify appropriate times for possible intervention. Third, threshold concepts observed across disciplines may or may not mask important nuances that apply within specific disciplinary contexts. The current study therefore employs a novel Bayesian knowledge tracing (BKT) approach to identify possible threshold concepts using a large data set from the biological sciences. Using rubric-scored samples of doctoral students’ sole-authored scholarly writing, we apply BKT as a strategy to identify potential threshold concepts by examining the ability of performance scores for specific research skills to predict score gains on other research skills. Findings demonstrate the effectiveness of this strategy, as well as convergence between results of the current study and more conventional, qualitative results identifying threshold concepts at the doctoral level.
How do higher education students regulate their learning with video modeling examples, worked examples, and practice problems?
Presenting novices with examples and problems is an effective and efficient way to acquire new problem-solving skills. Nowadays, examples and problems are increasingly presented in computer-based learning environments, in which learners often have to self-regulate their learning (i.e., choose what type of task to work on and when). Yet, it is questionable how novices self-regulate their learning from examples and problems, and to what extent their choices match with effective principles from instructional design research. In this study, 147 higher education students had to learn how to solve problems on the trapezoidal rule. During self-regulated learning, they were free to select six tasks from a database of 45 tasks that varied in task format (video examples, worked examples, practice problems), complexity level (level 1, 2, 3), and cover story. Almost all students started with (video) example study at the lowest complexity level. The number of examples selected gradually decreased and task complexity gradually increased during the learning phase. However, examples and lowest level tasks remained relatively popular throughout the entire learning phase. There was no relation between students' total score on how well their behavior matched with the instructional design principles and learning outcomes, mental effort, and motivational variables.
Interactive Learning Environments
Comparison of undergraduate students’ experiences in a flipped course pre-pandemic and during the COVID-19 pandemic
Nursing students’ experiences of operating room practice conducted through distance education within the scope of social cognitive career theory during the COVID-19 pandemic: A phenomenological qualitative study
Enhance affective expression and social reciprocity for children with autism spectrum disorder: using virtual reality headsets at schools
Profiling high school students’ multimodal posting in a digital literacy SPOC and examining teachers’ and students’ perceptions
The emotional design of an instructor: body gestures do not boost the effects of facial expressions in video lectures
Influencing factors of students’ small private online course-based learning adaptability in a higher vocational college in China
Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: a case of CSIEC chatbot
Unleashing the power of Open Educational Practices (OEP) through Artificial Intelligence (AI): where to begin?
Teaching cooperative learning through cooperative learning environment: a qualitative follow-up of an experimental study
Influencing factors and prospects of electronic service implementation in higher educational institutions of Ethiopia
Design of a learning dashboard to enhance reading outcomes and self-directed learning behaviors in out-of-class extensive reading
Emotion, cognitive load and learning achievement of students using e-textbooks with/without emotional design and paper textbooks
Factors influencing EFL teachers’ implementation of SPOC-based blended learning in higher vocational colleges in China: A study based on grounded theory
The impact of COVID-19 induced anxiety on students’ engagement while learning with online computer-based simulations: the mediating role of boredom
Using a holographic application in learning medical terminology for English as a foreign language students
Artificial intelligence-supported art education: a deep learning-based system for promoting university students’ artwork appreciation and painting outcomes
International Journal of Computer-Supported Collaborative Learning
In today’s digital society, computer-supported collaborative learning (CSCL) and collaborative problem solving (CPS) have received increasing attention. CPS studies have often emphasized outcomes such as skill levels of CPS, whereas the action transitions in the paths to solve the problems related to these outcomes have been scarcely studied. The patterns within action transitions are able to capture the mutual influence of actions conducted by pairs and demonstrate the productivity of students’ CPS. The purpose of the study presented in this paper is to examine Finnish sixth graders’ (N = 166) patterns of action transitions during CPS in a computer-based assessment environment in which the students worked in pairs. We also investigated the relation between patterns of action transitions and students’ social and cognitive skill levels related to CPS. The actions in the sequential processes of computer-based CPS tasks included using a mouse to drag objects and typing texts in chat windows. Applying social network analysis to the log file data generated from the assessment environment, we created transition networks using weighted directed networks (nodes for those actions conducted by paired students and directed links for the transitions between two actions when the first action is followed by the second action in sequence). To represent various patterns of action transitions in each transition network, we calculated the numbers of nodes (numbers of actions conducted), density (average frequency of transitions among actions), degree centralization (the dispersion of attempts given to different actions), reciprocity (the extent to which pairs revisit the previous one action immediately), and numbers of triadic patterns (numbers of different repeating formats within three actions). The results showed that pairs having at least one member with high social and high cognitive CPS skills conducted more actions and demonstrated a higher average frequency of action transitions with a higher tendency to conduct actions for different number of times, implying that they attempted more paths to solve the problem than the other pairs. This could be interpreted as the pairs having at least one student with high social and high cognitive CPS skills exhibiting more productive CPS than the other pairs. However, we did not find a significant difference across the pairs in terms of alternating sequences of two or three actions. Investigating the patterns of action transitions of the dyads in this study deepens our understanding of the mutual influence between the CPS actions occurring within dyads. Regarding pedagogical implication, our results offer empirical evidence recommending greater awareness of the students’ social and cognitive capacities in CPS when assigning them into pairs for computer-based CPS tasks. Further, this study contributes to the methodological development of process-oriented research in CSCL by integrating an analysis of action transition patterns with a skill-based assessment of CPS.
Research on computer-supported collaborative learning faces the challenge of extending student collaboration to higher social levels and enabling cross-boundary interaction. This study investigated collaborative knowledge building among four Grade 5 classroom communities that studied human body systems with the support of Idea Thread Mapper (ITM). While students in each classroom collaborated in their local (home) discourse space to investigate various human body functions, they generated reflective syntheses— “super notes”—to share knowledge progress and challenges in a cross-community meta-space. As a cross-community collaboration, students from the four classrooms further used the Super Talk feature of ITM to investigate a common problem: how do people grow? Data sources included classroom observations and videos, online discourse within each community, students’ super notes and records of Super Talk discussion shared across the classrooms, and student interviews. The results showed that the fifth-graders were able to generate high quality super notes to reflect on their inquiry progress for cross-classroom sharing. Detailed analysis of the cross-classroom Super Talk documented students’ multifaceted understanding constructed to understand how people grow, which built on the diverse ideas from each classroom and further contributed to enriching student discourse within each individual classroom. The findings are discussed focusing on how to approach cross-community collaboration as an expansive and dynamic context for high-level inquiry and continual knowledge building with technology support.
Collaborative analytics-supported reflective Assessment for Scaffolding Pre-service Teachers’ collaborative Inquiry and Knowledge Building
Helping pre-service teachers (PSTs) develop competencies in collaborative inquiry and knowledge building is crucial, but this subject remains largely unexplored in CSCL. This study examines the design and process of collaborative analytics-supported reflective assessment and its effects on promoting PSTs to develop their competencies in collaborative inquiry and knowledge building. We used a quasi-experimental design that lasted 18 weeks. The experimental group was a class of 40 PSTs who took a liberal studies course with a knowledge building design enhanced by collaborative analytics-supported reflective assessment. The comparison group was a class of 28 PSTs taught by the same instructor who studied the same inquiry topics but experienced a regular knowledge building environment using portfolios. The analysis of the PSTs’ Knowledge Forum discourse showed that collaborative analytics-supported reflective assessment helps PSTs develop collaborative inquiry competencies for community knowledge advancement. The analysis of the PSTs’ reflection using collaborative analytics and prompt questions showed that the design using KBDeX visualization and knowledge building rubrics helped them engage in productive collaborative knowledge building inquiry by involving them in continuous monitoring, analysis, negotiation, synthesis of inquiry, identification of promising routes for inquiry, and actions to guide further collective inquiry. Implications for designing CSCL collaborative-analytics enriched with reflective assessment and student agency, and broadening CSCL and knowledge building approaches to pre-service teacher education are discussed.
Computer-supported collaborative learning (CSCL) environments may at times become socio-emotionally tense and pose challenges that may have detrimental effects on mutual trust and shared mental models. Objective. This study aims to examine and classify general teamwork challenges in a novel but authentic CSCL setting (hackathon) to identify challenges that impede the development of key team coordination mechanisms (i.e., mutual trust and shared mental models). Methods. Multimodal data including responses to an adapted questionnaire (AIRE), post-competition interview data, and videos of team interaction were collected during an international hackathon event (N = 48, 71% male, M = 22 years age). Qualitative theory-driven coding and theme development were used to analyze the multimodal dataset (Greeno, 2006; Jarvenoja et al., 2013). Results. Our analyses revealed 16 general challenges that hamper teamwork in a hackathon. A model of team challenges was developed to categorize challenges into macro level themes including cognitive, motivational, emotional and behavioral challenges. We also identified which challenges hindered the development of mutual trust, and which challenges hindered the development of shared mental models. Conclusions. These findings provide important insights for educators and mentors in understanding the types of teamwork challenges that may occur in CSCL settings. The results also inform educators which challenges likely lead to mutual trust breakdown and weaken shared mental model bonds.