Computer-based Learning Environments Research Articles

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.

Nursing students’ satisfaction with the instructional design of a computer-based virtual learning environment for mathematical medication learning

ABSTRACT Computer-based virtual learning environments (CBVLEs) have attracted attention as a learning innovation that can foster students’ self-efficacy and intrinsic motivation. Research on the instructional design regarding these aspects of learning in a virtual learning environment is rather piecemeal. This study investigates the instructional design of a CBVLE for mathematical medication learning by nursing students in vocational education. The instructional design was based on a task-centered approach, and students’ future learning tasks formed the backbone. We examine the extent to which the CBVLE fostered the nursing students’ mathematical learning, self-efficacy, and intrinsic motivation, and the ways in which the design components of the CBVLE met nursing students’ satisfaction. In total, 118 nursing students were assigned to four groups, with or without extra support from worked examples, and were trained via the CBVLE on mathematical medication learning tasks over four consecutive weeks. Students were pre- and post-tested on their mathematical medication learning, self-efficacy, and intrinsic motivation. Students also rated their satisfaction with the instructional design. Our results showed that the CBVLE fostered nursing students’ mathematical medication learning, self-efficacy, and intrinsic motivation, but no significant differences were found between the four conditions. Overall, student satisfaction was above average. The design components were able to predict nursing students’ mathematical medication learning, self-efficacy, and intrinsic motivation.

In search of a measure to address different sources of cognitive load in computer-based learning environments

In search of a measure to address different sources of cognitive load in computer-based learning environments

Identification of Effective Scaffolding to Novices Using CBLE

Abstract: The aim of this study is to discover which kind of scaffolding can effectively promote learning. The past studies have shown mixed results in this regard. The process in which a domain expert gives and withdraws support in order to make a novice learner complete the task is known as scaffolding. A total of four distinct scaffold combinations and four groups were made. This experimental study was repeated twice to cross verify the outcomes using computer based learning environment (CBLE). The CBLE was designed with intelligent web program in PHP and jQuery to evaluate the solutions submitted by the learners instantly. The CBLE acted as an intelligent feedback system. In the first study, it was found that there was a significant effect of different scaffolding treatments on the learning outcomes, F (3,76) = 5.762, p=.001. The result analysis involves multiple comparisons based on Tukey HSD test and indicated that the mean score for the indirect support and adaptive fading (M=4.45, SD=1.191) was considerably different than the others. Likewise, second study also found that there was a significant effect of different scaffold treatments on the learning outcome, F (3,76) = 4.258, p=.008. The Tukey HSD test applied during the second study indicated that the mean score for the indirect support and adaptive fading (M=4.55, SD=1.19) was again significantly different than the others. The present study additionally measured the flow state of all the four groups using Kruskal-Wallis H test and found that indirect support and adaptive fading group was significantly different than direct support and adapting fading group as well as direct support and gradual fading group in both the studies. Keywords:Computer Based Learning Environment (CBLE),

The evolution of metacognitive strategy use in an open-ended learning environment: Do prior domain knowledge and motivation play a role?

There is a growing interest in viewing self-regulated learning as events unfolding over time, especially when students perform learning tasks in computer-based environments. Metacognitive activities are critical events in self-regulated learning. This study investigated the evolution of metacognitive strategy use in an open-ended computer-based learning environment, Betty's Brain. The data were from 93 sixth graders who used Betty's Brain to learn about climate change for four days. We extracted indicators of metacognitive strategy use from action logs. A knowledge test and self-report questionnaire were administrated before students started using Betty's Brain to assess prior domain knowledge and motivation, respectively. Results showed that metacognitive strategy use increased from the first to the second day and remained stable from the second to the fourth day of the study. The evolution of these behaviors varied across students. Task value and prior domain knowledge partially explained the individual differences in this evolution. Task value and prior domain knowledge also predicted the use of metacognitive strategies. Self-efficacy did not influence metacognitive strategy use. These results suggest the need for further investigation into the role of motivation and prior domain knowledge in the temporal evolution of metacognitive events.

What happens to your body during learning with computer-based environments? Exploring negative academic emotions using psychophysiological measurements

This explorative study aims to examine if electrodermal activity (EDA) and heart rate (HR) are appropriate measures for identifying and monitoring academic emotions during learning in computer-based learning environments (CBLEs). Understanding learners' emotions while using CBLEs, allows improving the design of CBLEs. Therefore, we collected EDA, HR, and self-report data from 32 participants to measure academic emotions during learning with CBLEs in a laboratory setting. We induced negative academic emotions during learning using harmful connotated learning content about animal welfare. In a pre-post design, participants reported their emotional state before and after learning. We collated the self-reports with the EDA and HR curves to identify the emotional change in real-time. We prepared the data for repeated measurement analyses and group differences (high-, middle-, low learning performance; bored vs. not bored participants). Negative academic emotions were detected in increased EDA and HR. EDA turned out to be an indicator of learning performance. Boredom manifested in HR decrease. Findings show that EDA and HR are appropriate tools to measure academic emotions. We want to show the importance of real-time measures for learning and the efficiency of EDA and HR measures. It is worth considering EDA as a predictor for learning success and implementing EDA and HR measurements in CBLEs. However, more research is needed to clarify the role of HR in the context of learning performance.

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.

The trade-off between complexity and accuracy. Preparing for computer-based adaptive instruction on fractions

ABSTRACT PS-I learning approaches with an initial problem-solving phase (PS) followed by an instruction phase (I) can be optimised by providing adaptive (individualised) instruction that builds on students’ problem-solving products. A computer-based implementation of both phases offers the possibility of adaptivity at the transition from the problem-solving to the instruction phase. However, it also poses two challenges for the problem-solving phase, which should allow for (1) complex problem-solving processes (process validity) and (2) accurate diagnosis of problem-solving products. Increased openness of the learning environment can increase the process validity and decrease diagnostic accuracy and vice-versa (complexity-accuracy trade-off). Using two variants of a computer-based learning environment for fraction comparison with different extent of openness (O+ and O-), we investigate whether both requirements (complex problem-solving processes and accurate diagnosis of the problem-solving products) can be met simultaneously. To check process validity (complexity of problem-solving processes), we compare the problem-solving products with those from previous research on an analogous paper-based version. Our results show that valid and comparable problem-solving processes can be achieved in both computer-based learning environments (O+ and O-). Moreover, only the learning environment O- enables an accurate computer-based interpretation of students’ products and thus resolving the complexity-accuracy trade-off.

Using metacognitive prompts to enhance self‐regulated learning and learning outcomes: A meta‐analysis of experimental studies in computer‐based learning environments

AbstractBackgroundIt has been assumed that prompting students to plan, monitor and evaluate their learning process could stimulate strategy use and thereby improve learning outcomes.ObjectivesThis study aimed to examine the effects of metacognitive prompts on students' self‐regulated learning (SRL) and learning outcomes in the context of computer‐based learning environments (CBLEs).MethodsTo achieve this, the current study took a meta‐analytic approach to critically evaluate evidence for the effectiveness of metacognitive prompts and identify potential moderators of the effects.Results and conclusionsWith random‐effects models, the results showed that metacognitive prompts significantly enhanced SRL activities (g = 0.50, 95% confidence interval [0.37, 0.63]) and learning outcomes (g = 0.40, 95% confidence interval [0.31, 0.49]) relative to the control conditions. Furthermore, moderator analyses revealed that the effects varied as a function of three prompts features: feedback, specificity and adaptability.ImplicationsDeveloping task‐specific, individual‐adaptive prompts and feedback should be a design principle in CBLEs, such that the prompt effect could be retained, sustainably enhanced and transferred to novel situations.

The Role of Self-Improving Tutoring Systems in Fostering Pre-Service Teacher Self-Regulated Learning.

Computer-based learning environments serve as a valuable asset to help strengthen teacher preparation and preservice teacher self-regulated learning. One of the most important advantages is the opportunity to collect ambient data unobtrusively as observable indicators of cognitive, affective, metacognitive, and motivational processes that mediate learning and performance. Ambient data refers to teacher interactions with the user interface that include but are not limited to timestamped clickstream data, keystroke and navigation events, as well as document views. We review the claim that computers designed as metacognitive tools can leverage the data to serve not only teachers in attaining the aims of instruction, but also researchers in gaining insights into teacher professional development. In our presentation of this claim, we review the current state of research and development of a network-based tutoring system called nBrowser, designed to support teacher instructional planning and technology integration. Network-based tutors are self-improving systems that continually adjust instructional decision-making based on the collective behaviors of communities of learners. A large part of the artificial intelligence resides in semantic web mining, natural language processing, and network algorithms. We discuss the implications of our findings to advance research into preservice teacher self-regulated learning.

A New Approach to Modelling Students’ Socio-Emotional Attributes to Predict Their Performance in Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called SoEmo-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.

Internship of Accounting Students in the Form of E-Learning: Insights from Poland

The COVID-19 pandemic has imposed on us not only e-learning with higher education providers, but also triggered considerable difficulties in organization internships. Institutions and enterprises that used to be eager to take interns have refused to do so. In these conditions, the key objective for the Faculty of Management at the UTP University in Bydgoszcz was to organize e-internships to ensure a working environment similar to the real working conditions of the accounting department. A new internship program was developed that implemented the assumptions of active learning, by virtue of the case study approach, computer-based learning environments, and a comprehensive task simulating the work of the accounting department in the form of a multi-step project. The key objective of this article is to present the results of research in the form of a survey on how the students majoring in Finance and Accounting perceive the proposed internship method. The results show that an internship in the form of e-learning with the proposed education methods is appreciated by the students. E-learning does not necessarily have to be less effective than traditional learning. The use of platforms and the selecting of adequate methods can enhance the activity of students, supporting self-education and independent task performance.

Understanding self-regulated learning in Singapore’s social studies classrooms

ABSTRACT Self-regulated learning, reflective thinking, and meta-cognition have been given greater emphasis in Singapore’s education system as central to the development of 21st century competencies and lifelong learning capacities. This study explores students’ perceptions of their Self-Regulated Learning (SRL) strategies in Secondary Social Studies after engaging in a Computer-Based Learning Environment (CBLE) lesson with online information sources. Semi-structured group interviews were used to tease out the SRL strategies utilised by students from two different schools. SRL strategies were embedded in the CBLE as instructional prompts created for the purpose of assisting students during task completion. Students were then asked to reflect and comment on their work to identify SRL strategies they utilised in their learning. Findings demonstrate a range of SRL behaviours conveyed by students in the areas of planning for learning tasks, monitoring their learning, and reflection on their learning. The study highlights the importance of SRL strategies in Social Studies education to improve student learning.

The incubation effect among students playing an educational game for physics

The incubation effect (IE) is a problem-solving phenomenon composed of three phases: pre-incubation where one fails to solve a problem; incubation, a momentary break where time is spent away from the unsolved problem; and post-incubation where the unsolved problem is revisited and solved. Literature on IE was limited to experiments involving traditional classroom activities. This initial investigation showed evidence of IE instances in a computer-based learning environment. This paper consolidates the studies on IE among students playing an educational game called Physics Playground and presents further analysis to examine the incidence of post-incubation or the revisit to a previously unsolved problem. Prior work, which focused on predicting successful outcomes, includes a coarse-grained IE model developed with logistic regression on aggregated data and an improved model which leveraged long short-term memory (LSTM) combined with dimensionality reduction visualization technique and clustering on fine-grained data. The additional analysis which aims to understand factors that may trigger the post-incubation phase also used fine-grained data and LSTM to create a revisit model. Results show that time elapsed relative to the activity period and encountering a problem with a similar solution during incubation were possible factors in revisiting previously unsolved problems.

COMPUTER-BASED MANAGEMENT OF PRE-SERVICE TEACHERS' INDEPENDENT WORK

The paper is devoted to the issue of managing independent work of students of pedagogical universities under the conditions of educational informatization. The conducted analysis of the literature identifies such key aspects of students’ independent work as independence (self-regulation) of students and its orientation by the teacher. This requires finding effective ways to manage educational and cognitive activities of students based on widespread use of information and communication technologies, especially under the current conditions of blended and distance learning, i.e., the transition to computer-based management of students’ independent work. There is a review of the main approaches to the defining of “independent work of students” and “management of students’ learning activities”. The functional model of management of students’ independent work is presented. Following the defined model, it is noted about the importance of achieving an effective combination of management of students’ cognitive activity by the teacher as well as their own self-management. The authors emphasize the importance of increasing the independence level at the end of learning disciplines and studying at the university. It is focused on need to use a specially designed learning environment of the university for the effective implementation of computer-based management of students’ independent work. Besides, there is a focus on necessity in preparing both academic staff and students for managing students’ independent work and using information and communication technologies. The main components of the computer-based learning environment for managing students' independent work of the National Pedagogical Dragomanov University are reviewed. The authors have analyzed the findings of the survey on the use of computer-based tools for managing students’ independent work by the academic staff. The main advantages of their use have been also clearly determined.Keywords: pre-service teachers; independent work of students; management of students' independent work; computer-based management; computer-based learning environment.

The functional roles of metacognitive judgement and emotion in predicting clinical reasoning performance with a computer simulated environment

ABSTRACT Monitoring one’s learning activities is a key component of self-regulated learning (SRL) leading to successful learning and performance outcomes across settings. Achievement emotions also play an important part in SRL and consequently student learning outcomes. However, there is little research on how specific types of monitoring (i.e. Feeling-of-Knowing, Judgement-of-Learning) and achievement emotions jointly affect performance in clinical reasoning within computer simulated environments. In this study, we explored the joint predictive value of monitoring judgements and achievement emotions in medical students’ ability to make accurate and efficient clinical diagnoses using BioWorld, a computer-based learning environment designed to help medical students practice clinical reasoning skills. Multiple types of data (log files, verbal protocols, self-report questionnaires) from 27 students were analyzed using text mining and linear mixed-effects models. We found that feeling-of-knowing judgements positively predicted diagnostic efficiency whereas judgement-of-learning and the achievement emotion of anger negatively predicted diagnostic efficiency. Achievement emotion of pride positively predicted confidence in diagnosing the case correctly. This study not only provides theoretical and methodological insights but also alert medical instructors to the many dimensions of diagnostic performances and effective intervention strategies in clinical reasoning.

An Electronic Collaborative Learning Environment for Standardized Tests

The National Center for Measurement (Qiyas) was established for the purpose of conducting standardized tests in Saudi Arabia. The center has developed multiple Qiyas tests such as the General Aptitude Test (GAT) and the Scholastic Achievement Admission Test (SAAT). Qiyas tests are used as a pre-requisite requirement for applying to universities and to certain kinds of jobs in Saudi Arabia. Currently, students use traditional methods to prepare for these tests, e.g., studying from books and searching for available learning resources on the internet. These web-based resources are mostly static and only have general guidelines about the tests and a history of available test samples. This research proposes a computer-based collaborative learning (CL) environment that helps support learners during their preparation for the Qiyas tests. A four-stage approach is used in this research: (1) an intensive review of 30 CL platforms is carried out to investigate the available features, (2) two workshops are conducted to evaluate the appropriateness of the features identified in Stage 1 as well as to investigate what other features would be appropriate for Qiyas tests, (3) a CL platform is developed for Qiyas tests for a total of 21 features, (4) and, lastly, the platform is evaluated using two methods, in-depth interviews with experts and an empirical study with instructors and learners. The results show that the platform helps support learners in the participative, cognitive, interactive, and social dimensions of the learning processes. The results also help instructors promote the teaching experience.

The effects of computer-based virtual learning environments on nursing students\u2019 mathematical learning in medication processes

Computer-based virtual learning environments (CBVLEs) are potentially useful teaching tools for training nursing students in professional duties such as the mathematical tasks associated with medication processes. In this study, a CBVLE was designed with well-structured instructional activities such as interleaved practice and feedback. Mathematical medication scenarios and basic arithmetic exercises were integrated into the CBVLE. Four training conditions were used in the CBVLE to facilitate extra support for mathematical medication learning: (1) learning without worked examples, (2) learning with worked examples involving domain-specific knowledge, (3) learning with worked examples involving regular thinking strategies, and (4) learning with combined worked examples. This study was conducted with 118 nursing students enrolled in post-secondary nursing education and Bachelor’s nursing programmes. Students were pre-tested and post-tested on their mathematical medication learning. Training in the CBVLE improved mathematical medication learning for all students from pre-test to the post-test stages, but no differences were found among the four different conditions. Nursing students’ prior knowledge, non-verbal intelligence, and number of correct tasks predicted mathematical medication learning outcomes. When controlling for non-verbal intelligence, students in the condition 1 benefited more than students in condition 3 in terms of their mathematical medication learning outcomes. The same accounted for the support of the low-achieving students in the CBVLE. The support conditions for the high-achieving group appeared to be unimportant for mathematical medication learning. It seems that technology is taken over some of the capacity of working memory, which accounts for the benefits to the low-achieving learners.