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Tag: Computing Education

  • Report method linked to critical thinking and presentation skills

    Report method linked to critical thinking and presentation skills

    What the study found

    The study found that the report method, a teaching approach based on student presentations or reports, has both advantages and challenges in informatics and computer science education. The authors also state that it affects the development of students' critical thinking and presentation skills.

    Why the authors say this matters

    The authors conclude that understanding the effectiveness of the report method matters for improving the educational process in informatics and computer science. They say the study provides insight and recommends ways to improve the method.

    What the researchers tested

    The researchers examined the role of the report method as an educational strategy in teaching informatics and computer science. They analyzed its advantages, challenges, and its impact on critical thinking and presentation skills.

    What worked and what didn't

    The abstract says the report method has advantages, but it also presents challenges. It does not specify which advantages or challenges were found, or how strong the effects on students' skills were.

    What to keep in mind

    The available summary does not provide detailed methods, measurements, or specific results. Limitations are not described beyond the general note that the method has both advantages and challenges.

    • The study examines the report method in teaching informatics and computer science.
    • The authors describe the method as having both advantages and challenges.
    • The abstract says the method affects students' critical thinking and presentation skills.
    • The authors aim to provide insight into the method's effectiveness.
    • The paper recommends ways to improve the report method in education.

  • Pre-course aptitude test predicted introductory programming performance

    What the study found

    The study found that a pre-course aptitude test had some ability to predict first-year computer science students’ performance on an introductory programming assessment. The authors report that a Random Forest Regressor performed more consistently than a Random Forest Classifier, though there was still a sizeable margin of error.

    Why the authors say this matters

    The authors suggest that early identification of students who may struggle with programming could help direct additional support from the outset. They also conclude that their approach offers a foundation for future targeted support interventions in introductory programming modules.

    What the researchers tested

    The researchers studied 285 first-year computer science undergraduates and developed a pre-course aptitude test. The test collected information on students’ backgrounds, prior experience, perceived confidence, and likelihood of holding appropriate mental models, meaning internal understandings, of core programming concepts. They used the resulting data to train and validate regression and classification models, including Random Forest models refined with Sequential Feature Selection and tested on a holdout set.

    What worked and what didn't

    The Random Forest Classifier performed well during training, with AUC = 0.8688, F1 = 0.8353, and accuracy = 0.7450, but performance dropped on the hold-out test set to AUC = 0.7670, F1 = 0.7020, and accuracy = 0.7020. The authors interpret this as moderate overfitting, likely linked to class imbalance and limited data. The Random Forest Regressor showed similar performance in training and testing, with RMSE = 0.1616 and MAE = 0.1209 in training, and RMSE = 0.1713 and MAE = 0.1396 in testing.

    What to keep in mind

    The abstract says there is still a sizeable margin of error, so the predictions are not highly precise. It also notes possible overfitting in the classifier because of imbalanced classes and limited data. Other limitations are not described in the available summary.

    • A pre-course aptitude test was used to predict first introductory programming assessment results.
    • The study involved 285 first-year computer science students.
    • The aptitude test included background, prior experience, confidence, and mental-model measures.
    • The Random Forest Classifier overfit somewhat when moved from training to the hold-out test set.
    • The Random Forest Regressor was more consistent across training and testing.
    • The authors say the approach may help identify students needing extra support early.

  • Experts validated an interdisciplinary AI engineering curriculum

    What the study found

    The study found that a newly developed interdisciplinary artificial intelligence (AI) engineering curriculum was expected to be effective, practical, and positively validated by educators and industry representatives. It also found that educators who helped design the program reported greater ownership and a stronger systemic understanding than those who did not participate.

    Why the authors say this matters

    The authors conclude that the study provides a validated transferable reference model for AI engineering programs. They also say it offers the first understanding of how participatory design may affect quality perceptions in interdisciplinary settings and provides practical guidance for institutions developing domain-specific AI programs.

    What the researchers tested

    The researchers evaluated the development of a new undergraduate AI engineering program worth 210 credits across seven semesters. They used formative evaluation, including curriculum mapping and focus group interviews with 19 experts, made up of educators and industry representatives, to examine perceived quality, consistency, practicality, and effectiveness.

    What worked and what didn't

    The abstract says the conceptual program was viewed as likely to be effective and practical, with positive validation from educators and industry. It also reports that the interdisciplinary structure was seen as a strength for employability, while stakeholders identified practical challenges that would need attention during implementation.

    What to keep in mind

    The summary describes perceptions of the program rather than a full implementation outcome. It also notes that practical challenges were identified, but the abstract does not specify all of them.

    • A new undergraduate AI engineering curriculum was developed as a 210-credit, seven-semester program.
    • Curriculum mapping and focus group interviews with 19 experts were used to evaluate the program.
    • Educators and industry representatives viewed the curriculum as effective and practical.
    • Educators who took part in design reported more ownership and systemic understanding than nonparticipants.
    • The interdisciplinary structure was seen as supporting employability, but implementation challenges remained.

  • AI literacy training improved teacher education students’ self-efficacy

    What the study found

    The study found that a GenAI (generative artificial intelligence) literacy training workshop was associated with significant gains in teacher education students' AI competence self-efficacy, attitudes toward GenAI, and commitment to critical, ethical, and pedagogical engagement with GenAI tools. The authors also report design principles for AI literacy training in teacher education.

    Why the authors say this matters

    The authors conclude that teacher education programmes need GenAI literacy that supports teachers' changing roles as reflective practitioners, co-creators, and lifelong learners in an AI-driven world. They also say the findings support integrating training that includes technological, ethical, sociocultural, and human-centered dimensions.

    What the researchers tested

    This design-based research study developed and evaluated a set of design principles for GenAI literacy training in teacher education. The principles were implemented in a workshop prototype that was first piloted with 14 master's students and then evaluated with 29 teacher education students.

    What worked and what didn't

    The workshop was linked to significant gains in participants' AI competence self-efficacy, attitudes toward GenAI, and commitment to critical, ethical, and pedagogical engagement with GenAI tools. The abstract also says the study identified key principles for AI literacy training and used active, experiential, and transformative learning approaches. It does not report any outcome that failed to improve in the summary provided.

    What to keep in mind

    The summary provided does not include detailed measures, effect sizes, or information about how long the gains lasted. It also does not describe limitations beyond noting that existing AI literacy programmes often lack pedagogically structured frameworks and that teacher education faces time and faculty literacy constraints.

    • A GenAI literacy workshop was associated with higher AI competence self-efficacy among teacher education students.
    • Participants also showed more positive attitudes toward GenAI after the training.
    • The study reports increased commitment to critical, ethical, and pedagogical engagement with GenAI tools.
    • The researchers developed and evaluated design principles for GenAI literacy training using design-based research.
    • The workshop was first piloted with 14 master's students and then evaluated with 29 teacher education students.

  • Micro:Bit gamification improved emotions and readiness for computational thinking

    What the study found

    The study found that a gamified low-code programming approach using the micro:bit, a physical computing device, was associated with better emotional responses and improved readiness for computational thinking and mathematical thinking. The findings also included pre-service teachers' perspectives within sustainable mathematics education.

    Why the authors say this matters

    The authors say the approach may help address gaps in preparation for teaching computational thinking and managing the affective, or emotion-related, aspects of learning. They also suggest the findings may be adaptable across different academic and professional domains under predictable and uncertain conditions.

    What the researchers tested

    The researchers investigated low-code programming with the micro:bit in a gamified context for P–12 students, meaning students from pre-kindergarten through grade 12, within sustainable mathematics education. They also considered the perspectives of pre-service teachers, or people preparing to become teachers, in relation to computational thinking and mathematical thinking.

    What worked and what didn't

    Positive emotions increased and negative emotions decreased after the intervention, except for frustration and boredom. Interest in and engagement with perceived readiness for computational thinking and mathematical thinking also improved among pre-service teachers.

    What to keep in mind

    The abstract describes the study as underexplored and insufficiently evaluated, but it does not give detailed limitations, sample size, or measurement methods in the available summary. The reported effects are based on the intervention described in the abstract and should be read within that scope.

    • A gamified micro:bit-based low-code approach was studied in sustainable mathematics education.
    • Positive emotions increased after the intervention, while negative emotions decreased except for frustration and boredom.
    • Pre-service teachers showed improved interest in and engagement with readiness for computational thinking and mathematical thinking.
    • The authors frame the work as addressing gaps in teacher preparation and affective aspects of learning.
    • The abstract does not provide detailed limitations or study size.

  • Micro:bit-based activities improved pre-service teachers’ self-perceived CT knowledge

    What the study found

    The study found significant increases in pre-service mathematics teachers' self-perceived computational thinking (CT, the ability to solve problems using step-by-step, algorithmic thinking) content knowledge and positive attitudes after a micro:bit-based intervention. Two negative emotions, frustration and boredom, persisted regardless of individual characteristics.

    Why the authors say this matters

    The authors conclude that integrating CT into pre-service teacher education in higher education institutions may be beneficial for future K-12 mathematics instruction. They also say the findings support the internal consistency and preliminary construct validity of the measurement instruments used.

    What the researchers tested

    The researchers studied 228 pre-service mathematics teachers in various courses using a pre-experimental design. The intervention followed a six-step sequence: introducing CT concepts through divisibility content, moving to visual block programming and Python coding, and ending with a physical calculator for identifying factors and divisors.

    What worked and what didn't

    The intervention was associated with significant increases in self-perceived CT content knowledge and positive attitudes. Frustration, described as an activating negative emotion, and boredom, described as a deactivating negative emotion, persisted across the sample and did not vary by individual characteristics.

    What to keep in mind

    The abstract does not describe detailed limitations beyond noting that the participants were initially underprepared to teach CT concepts. The results are based on self-perceived measures and a pre-experimental design, as stated in the abstract.

    • 228 pre-service mathematics teachers took part in courses using a pre-experimental design.
    • A six-step intervention used divisibility content, visual block programming, Python coding, and a physical calculator activity.
    • Self-perceived CT content knowledge increased significantly after the intervention.
    • Positive attitudes also increased significantly.
    • Frustration and boredom persisted regardless of individual characteristics.
    • The authors say the findings support the potential benefits of CT integration in pre-service teacher education.