Computer-based Learning Environments Research Articles

Achievement Emotions and Academic Performance: On the Computer-based Learning Environments

Achievement Emotions and Academic Performance: On the Computer-based Learning Environments

Effectiveness of Automated Formative Feedback in an Online Tutorial for Promoting Summarizing

We conducted a study with the aim to investigate the effectiveness of automated formative feedback in improving students’ ability to summarize. One-hundred and thirty-eight undergraduate students in an elementary education program were asked to summarize six scientific texts, with the experimental group (N=87) receiving automated formative feedback in a computer-based learning environment (FALB). FALB provides automated feedback about content coverage, copying words avoidance, redundancy avoidance, relevance, and length. Comparing the experimental group to a control group (N=51), results implied that summarizing skills could be fostered when interacting with FALB. In particular, the automated formative feedback promoted the adherence to the predefined length and the avoidance of copying words while maintaining a high content coverage, fostering cognitive processes essential for constructing a mental model of a text. In addition, students in the experimental group were able to maintain high quality summaries in their final session when not scaffolded. In conclusion, FALB supports the alignment of internal standards with external standards and provides an incentive to revise and engage with texts.

Multilingual Computer-Based Learning Environments For Stem Education And Language Awareness

Multilingual Computer-Based Learning Environments For Stem Education And Language Awareness

The impact of cognitive and motivational resources on engagement with automated formative feedback

The effectiveness of automated formative feedback highly depends on student feedback engagement that is largely determined by learners’ cognitive and motivational resources. Yet, most studies have only investigated either cognitive resources (e.g., mental effort), or motivational resources (e.g., expectancy-value-cost variables). The purpose of this study is to examine the development (indicated by time) and relationship of 1) cognitive, 2) affective, and 3) behavioral feedback engagement as a function of cognitive and motivational resources in a computer-based learning environment with automated formative feedback. Data was collected from N = 330 German B.Ed. Elementary Education students who worked four consecutive sessions on summarizing texts. Previously invested mental effort (t − 1) affected situational expectancy and cost but not situational value. 1) Cognitive feedback engagement was positively associated with previous performance but neither associated with cognitive nor motivational resources. 2) Affective feedback engagement was positively associated with intrinsic value and negatively associated with situational expectancies, invested mental effort and previous performance. 3) Behavioral feedback engagement was positively associated with situational expectancies and invested mental effort. This study contributes to the understanding of student’s cognitive and motivational structures when engaging with automated formative feedback.

Cognitive load patterns affect temporal dynamics of self-regulated learning behaviors, metacognitive judgments, and learning achievements

This study investigated how students' cognitive load patterns related to their self-regulated learning (SRL) and performance. One hundred and eleven medical students were required to accomplish a diagnostic task in a computer-based learning environment. The metacognitive aspects of SRL were reported by students at different points during the task, and cognitive load was rated after students finished the task. We extracted SRL behaviors from system logfiles, and students' performance was measured as diagnostic accuracy, which indicated whether they achieved an accurate diagnosis. A two-step cluster analysis was conducted to identify students’ cognitive load patterns based on their perceived intrinsic, extraneous, and germane load. We found three cognitive load patterns: knowledge builders with a low-level intrinsic and extraneous load but high-level germane load, task-loaded learners with a high-level intrinsic and extraneous load but low-level germane load, and overloaded learners with high-level intrinsic, extraneous, and germane load. Moreover, we performed epistemic network analyses to compare the SRL behavior networks of the three clusters. The results showed that knowledge builders exhibited stronger connections associated with monitoring behaviors, while task-loaded and overloaded learners constructed more connections associated with help-seeking behaviors. The transitions between evaluation and solution elaboration behaviors were more frequent for knowledge builders than for the other clusters. Furthermore, knowledge builders reported higher scores in two types of metacognitive judgments: ease-of-learning and judgment-of-learning. Knowledge builders also achieved significantly higher diagnostic accuracy than task-loaded and overloaded students. These findings contributed to a more comprehensive understanding of how cognitive load interacts with SRL.

Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving

AbstractLearning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal-labeled instructional materials in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. The backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic-proof construction. The training session involved backward worked examples (BWE) and problem solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students’ 1) experience with and engagement in learning backward strategy, 2) performance and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in posttest. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.

Pedagogical discourse markers in online algebra learning: Unraveling instructor's communication using natural language processing

Despite the proliferation of video-based instruction and its benefits—such as promoting student autonomy and self-paced learning—the complexities of online teaching remain a challenge. To be effective, educators require extensive training in digital teaching methodologies. As such, there's a pressing need to examine and comprehend the intricacies of instructors' communication patterns within this context. This research addresses the pressing need to understand pedagogical discourse in online video lectures in Algebra classes by employing computational linguistic tools and natural language processing (NLP). Using transcripts from 125 Algebra 1 video lectures—comprising 4962 instances of pedagogical discourse—from five instructors at Math Nation, a virtual math learning environment, we analyzed the conveyance of linguistic, attitudinal, and emotional nuances. With the aid of 26 Coh-Metrix and SÉANCE features, we classified educators' language choices, achieving an accuracy of 86.7%. Furthermore, variations in language choices, as signified by discourse markers, were examined through a K-means clustering approach. The resulting 17 clusters were grouped into interpersonal, structural, and cognitive pedagogic functions. Through this exploration, we demonstrate the promising potential of NLP in efficiently deciphering pedagogical communication patterns in video lectures. These insights open a new avenue for research, aimed at assessing the efficacy of digital instruction by scrutinizing pedagogical discourse characteristics in computer-based learning environments.

Supporting self-regulated learning in clinical problem-solving with a computer-based learning environment: the effectiveness of scaffolds

Supporting self-regulated learning in clinical problem-solving with a computer-based learning environment: the effectiveness of scaffolds

Investigating learning processes through analysis of navigation behavior using log files.

The empirical study investigates what log files and process mining can contribute to promoting successful learning. We want to show how monitoring and evaluation of learning processes can be implemented in the educational life by analyzing log files and navigation behavior. Thus, we questioned to what extent log file analyses and process mining can predict learning outcomes. This work aims to provide support for learners and instructors regarding efficient learning with computer-based learning environments (CBLEs). We evaluated log file and questionnaire data from students (N = 58) who used a CBLE for two weeks. Results show a significant learning increase after studying with the CBLE with a very high effect size (p < .001, g = 1.71). A cluster analysis revealed two groups with significantly different learning outcomes accompanied by different navigation patterns. The time spent on learning-relevant pages and the interactivity with a CBLE are meaningful indicators for Recall and Transfer performance. Our results show that navigation behaviors indicate both beneficial and detrimental learning processes. Moreover, we could demonstrate that navigation behaviors impact the learning outcome. We present an easy-to-use approach for learners as well as instructors to promote successful learning by tracking the duration spent in a CBLE and the interactivity.

Uncovering Patterns in Process Data to Analyze Interactions and Learning Outcomes Within a Computer-Based Learning Environment

Uncovering Patterns in Process Data to Analyze Interactions and Learning Outcomes Within a Computer-Based Learning Environment

DevEmo—Software Developers’ Facial Expression Dataset

The COVID-19 pandemic has increased the relevance of remote activities and digital tools for education, work, and other aspects of daily life. This reality has highlighted the need for emotion recognition technology to better understand the emotions of computer users and provide support in remote environments. Emotion recognition can play a critical role in improving the remote experience and ensuring that individuals are able to effectively engage in computer-based tasks remotely. This paper presents a new dataset, DevEmo, that can be used to train deep learning models for the purpose of emotion recognition of computer users. The dataset consists of 217 video clips of 33 students solving programming tasks. The recordings were collected in the participants’ actual work environment, capturing the students’ facial expressions as they engaged in programming tasks. The DevEmo dataset is labeled to indicate the presence of the four emotions (anger, confusion, happiness, and surprise) and a neutral state. The dataset provides a unique opportunity to explore the relationship between emotions and computer-related activities, and has the potential to support the development of more personalized and effective tools for computer-based learning environments.

How can signaling in authentic classroom videos support reasoning on how to induce learning strategies?

Classroom videos are a viable means to implement evidence-informed reasoning in teacher education in order to establish an evidence-informed teaching practice. Although learning with videos relieves pre-service teachers from acting in parallel and might reduce complexity, the material still poses higher cognitive load than written text vignettes or other traditionally used static material. In particular, the information they deliver is transient and can, therefore, easily be missed. Signaling can guide learners’ attention to central aspects of a video, thereby reducing cognitive load and enhancing learning outcomes. In the current project, pre-service teachers acquired scientific knowledge about learning strategies and their promotion in a computer-based learning environment. We explored the effect of different arrangements of signaling in classroom video-examples on conceptual knowledge and the reasoning-component of professional vision. Therefore, we conducted a set of two studies with 100 student teachers including two signal arrangements in order to investigate how signaling can help learning to reason about classroom videos. In addition, we varied if participants received information on the use of signals in advance (informed) or not (uninformed). We measured conceptual knowledge by asking participants what they knew about self-regulation strategies. Additionally, we assessed reasoning by asking participants to notice sequences in a video where teachers induced learning strategies, and to reason in what respect the observed behavior was useful to induce the strategy. Uninformed signaling did not affect the acquisition of conceptual knowledge and reasoning. Informed signaling led to significantly better conceptual knowledge than uninformed signaling. It is argued that the signal-induced extraneous load exceeded the load reduction due to the signal’s selection advantage in the uninformed conditions. In a third, exploratory study, nine participants were interviewed on the perception of different signals and indicated that spotlight and zoom-in signals foster processing of classroom videos.

The power of affective pedagogical agent and self-explanation in computer-based learning

The goal of the current study was to investigate the effects of affective pedagogical agent (PA) and student-generated explanation on learners' affective processing, visual attention, and learning outcomes during a video lesson. In a 2 (affective pedagogical agent: positive PA vs. neutral PA) × 2 (generative learning strategy: self-explanation vs. re-study) between-subjects design, participants watched an animation on synaptic transmission from a multimedia lesson that was either delivered by a positive PA or a neutral PA. After watching the video, students either generated a verbal explanation or rewatched the video. All students’ eye movements were tracked during learning and then took learning outcome tests. The results showed that: (a) students who learned with a positive PA paid more attention to relevant elements of the materials and performed better on learning outcomes than those who learned with a neutral PA; (b) self-explanation facilitated visual processing and learning outcomes; and (c) positive PA accompanying self-explanation increased intrinsic motivation and resulted in best learning performance. The findings support the use of a positive PA to design multimedia learning environments and the use of self-explanation strategy to engage students in generative processing activities. Moreover, students benefit most from positive PA and then generating an oral explanation, which demonstrates the importance of combining instructional design features and learners-generated learning activity in computer-based learning environments.

Providing Creativity Ineducational Enviroment During the Epochofartificial Intelligence: Philological Perspectives

This research explores the relevance and scientific novelty of conceptual features in the design of effective creative learning environments in the sphere of foreign philology. It emphasizes the often-overlooked extended intermediate phase of learning, proposing an advanced knowledge acquisition stage situated between initial creative learning and domain expertise. The article examines the interactive effects of contextual elements in various creative computer-based and AL learning environments while studying English language and addresses learning deficiencies associated with them. It underscores the importance of aligning learning and instruction with contextual factors. The relevance of this research is determined by its exploration of innovative practices, including technology integration, project-based learning, and AI applications in foreign philology. By addressing deficiencies in advanced knowledge acquisition, the research aims to guide philology and other specialists in adapting to 21st-century challenges in education and fostering inclusive creative and impactful learning environments. The scientific novelty of the research consists in its contribution into the educational field by proposing an advanced knowledge acquisition stage. It explores the dynamic interplay of contextual creative elements in computer-based learning, AI and hybrid educational environments while studying English language for special purposes, addressing learning failures and emphasizing the need for alignment with instructional design. This article aims to explore the structure of creative teaching methods and assessment strategies employed into the studying process of Professional English; to investigate and emphasize the critical relevance and scientific novelty of conceptual features in designing effective learning environments. It explores the often-overlooked extended intermediate phase of creative learning and introduces the concept of an advanced knowledge acquisition stage. The research delves into the interactive effects of contextual elements in various computer-based learning environments and identifies learning deficiencies associated with them. Furthermore, the article upholders for the alignment of learning and instruction with contextual factors. The methodology involves a comprehensive literature review on cognitive differences, contextual elements, and recent innovations in education. The ultimate goal is to contribute valuable insights to guide educational institutions in adapting to the 21st-century landscape and fostering inclusive and impactful creative learning environments. The purpose of this article is to investigate and underscore the critical relevance and scientific novelty of creative of using computer-based learning and Artificial intelligence in educational process, partially in studying Professional English, associated with conceptual features in the design of effective creative learning environments. This research aims to bring attention to the often-neglected extended intermediate phase of creative teaching and learning Professional English and introduce the concept of an advanced knowledge acquisition stage. Through an exploration of the interactive effects of contextual elements in various computer-based and AI creative learning environments, the article identify patterns of learning deficiencies. The methodology involves a comprehensive literature review focusing on cognitive differences, contextual elements, and recent innovations in education. Ultimately, the article provides valuable insights that reflects an experience of adapting to the teaching and learning Professional English in the epoch of AI. The methodology involves a comprehensive review of literature on cognitive differences between novice and expert learners, the interplay of contextual elements in learning environments, and recent innovations in education. The research also incorporates an analysis of patterns of deficiency in advanced knowledge acquisition and explores various strategies for incorporating innovations into university education.

Supporting the Flipped Classroom Approach to Higher Education Through a Computer-Based Learning Environment

This study exemplified two successful implementations of a flipped classroom approach using the computer-based learning environment Toolbox TeacherEducation (TTE) integrated into two separate university courses. We questioned how the TTE can be used in a flipped classroom to teach and learn successfully and how participants’ self-regulated learning and user experience contribute to learning. We analyzed two university courses (N1 = 34, N2 = 73) designed as flipped classrooms. To measure knowledge increase, we developed multiple-choice items to collect knowledge before and after learning. Participants showed a significant learning gain in both courses (average p = .025) with an average effect size of d = 1.02. Since self-regulated learning competencies and user experience affect computer-based learning, we addressed these concepts using different questionnaires. Regarding self-regulated learning, the participants reported above-average skills, but we did not find meaningful correlations with learning. Regarding user experience, the participants rated the TTE as highly usable and well designed. Based thereon, we showed how the TTE could be implemented in a flipped classroom to teach and learn successfully.

Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning

Self-Regulated Learning (SRL) is related to increased learning performance. Scaffolding learners in their SRL activities in a computer-based learning environment can help to improve learning outcomes, because students do not always regulate their learning spontaneously. Based on theoretical assumptions, scaffolds should be continuously adaptive and personalized to students' ongoing learning progress in order to promote SRL. The present study aimed to investigate the effects of analytics-based personalized scaffolds, facilitated by a rule-based artificial intelligence (AI) system, on students' learning process and outcomes by real-time measurement and support of SRL using trace data. Using a pre-post experimental design, students received personalized scaffolds (n = 36), generalized scaffolds (n = 32), or no scaffolds (n = 30) during learning. Findings indicated that personalized scaffolds induced more SRL activities, but no effects were found on learning outcomes. Process models indicated large similarities in the temporal structure of learning activities between groups which may explain why no group differences in learning performance were observed. In conclusion, analytics-based personalized scaffolds informed by students’ real-time SRL measured and supported with AI are a first step towards adaptive SRL supports incorporating artificial intelligence that has to be further developed in future research.

Missions with Monty: A Game-Based Learning Environment to Promote Comprehension Monitoring and Science Achievement

While game-based learning environments (GBLE) are an increasingly popular means to engage learners, there remains limited research on the benefits of GBLEs for students’ learning and metacognitive skills in classroom contexts. Missions with Monty is a GBLE developed to engage students as they learn comprehension strategies to support their scientific literacy with targeted Next Generation Science Standards content. The current missions focus on ecosystems, earth and human activity, molecules, and organisms. Students navigate through the game to solve challenges that require the application and transfer of science knowledge. One primary and unique focus of Missions with Monty is to benefit students’ metacognition through comprehension monitoring. Comprehension monitoring is an essential strategy to support literacy and is measured through students’ accuracy in rating their performance on comprehension items. In the game, students are explicitly taught and subsequently practice essential comprehension monitoring strategies. Additional key educational outcomes for students playing Missions with Monty include reading achievement, science knowledge, and transfer of science learning.&#x0D; In this study we examined 10 and 11 year old students’ outcomes as they either played the game (n = 144) or learned the content via a computer-based learning environment (CBLE) without game elements in a comparison condition (n = 80) within classrooms of diverse learners in the United States. Significant gains for science learning were found across conditions, (t(223) = 13.67, p &lt;.001). The primary research question of interest focused on whether students who played Missions with Monty were better able to monitor their understanding of science text after playing the game and therefore we compared monitoring, as measured by bias and accuracy, before and after the game play. Effective metacognition is demonstrated by low bias and high accuracy. Findings revealed that both conditions demonstrated improved bias (t(223) = 4.44, p &lt; .001) and accuracy (t(223) = 11.81, p &lt; .001). Thus, students were better able to monitor their science learning through the game, an essential skill for scientific literacy. Future research should continue to examine the benefits of GBLEs versus CBLEs in terms of benefits for motivation and learning.

Counting strategies of students struggling in mathematics in a computer-based learning environment

Children with mathematics difficulties often show challenges in additive reasoning and problem solving and may experience challenges with more advanced mathematics. To address these challenges, we implemented a computer-based intervention to promote children’s conceptual understanding of numbers, counting, and additive strategies. The purpose of this exploratory case study was to provide an in-depth analysis of the progression of counting and composite unit development as well as strategies for solving addition and subtraction problems by two third-graders with mathematics difficulties in a computer-based learning environment. We analyzed video-recorded sessions, field notes, and pre- and post-assessments. Our findings show that participating students negotiated solving addition and subtraction problems through inventing more advanced and efficient additive double-counting strategies. In addition, it seems that the growth of their counting through the computer-based intervention was generalized to solving additive problems on a paper-and-pencil test.

Understanding Clinical Reasoning through Visual Scanpath and Brain Activity Analysis

This paper presents an experimental study that analyzes learners’ visual behaviour and brain activity in clinical reasoning. An acquisition protocol was defined to record eye tracking and EEG data from 15 participants as they interact with a computer-based learning environment called Amnesia, a medical simulation system that assesses the analytical skills of novice medicine students while they solve patient cases. We use gaze data to assess learners’ visual focus and present our methodology to track learners’ reasoning process through scanpath pattern analysis. We also describe our methodology for examining learners’ cognitive states using mental engagement and workload neural indexes. Finally, we discuss the relationship between gaze path information and EEG and how our analyses can lead to new forms of clinical diagnostic reasoning assessment.

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.