Analyzing interviews on computational thinking for introductory physics students: Toward a generalized assessment

A systematic review of integrating computational thinking in early childhood education

Promoting computational thinking is a priority in CS education and other STEM and non-STEM disciplines. Our innovative, NSF-funded IC2Think project blends computational and creative thinking. In Spring 2013, we deployed Computational Creativity Exercises (CCE) designed to engage creative competencies (Surrounding, Capturing, Challenging and Broadening) in an introductory CSI course for engineering students. We compared this CCE implementation semester (80 students, 95% completing 3 or 4 CCEs) to the Fall 2013 semester of the same course (55 students) without CCEs. CCE implementation students had significantly higher scores on a CS concepts and skills knowledge test (F(1, 132) = 7.72, p < 01, partial Eta2 = .055; M=7.47 to M=6.13; 13 items) and significantly higher self-efficacy for applying CS knowledge in their field (F(1, 153) = 12.22, p < .01, partial Eta2 = .074; M=70.64 to M=61.47; 100-point scale). CCE implementation students had significantly higher study time (t(1, 136) = 2.08, p = .04; M=3.88 to M=3.29; 7-point scale) and significantly lower lack of regulation, which measures difficulties with studying (t(1, 136) = 2.82, p = .006; M=2.80 to M=3.21; 5-point scale). The addition of computational creativity exercises to CS courses may improve computational thinking and learning of CS knowledge and skills.

Computational thinking (CT) is an essential skill in the twenty-first century. The computational physics course (CPC) is one subject that is designed to support students in the practice of CT. Many studies show that the worksheets could be a solution in a CPC as a scaffold to achieve the CT objectives both online and offline. The study aims to develop the worksheet and integrate it with CT in a computational physics course. This study applied the research and development (R & D) method with the ADDIE model approach. In the results, the evaluation test from the experts reached a very good interpretation score based on the learning media expert (96%), the teaching material expert (95%), and the pedagogy experts (92%). So that this media is declared feasible to be used in the CPC. Furthermore, after the experimental study of students who took the computational physics course (n = 31), the study showed that the modified course could significantly improve student skills regarding overall CT (p value <0.05). However, this research also found that cooperative learning as part of CT had no improvement (p value >0.05). The experiment was conducted amid the COVID 19 pandemic wherein the students could only study at home for the whole semester. These findings indicate that the pandemic has impacted the collaborative skills of students on the course.

The lack of diversity and the under-performance of underrepresented students in STEM courses have been the focus of researchers in the last decade. In particular, many hypotheses have been put forth for the reasons for the under-representation and under-performance of women in physics. Here, we present a framework for helping all students learn in science courses that takes into account four factors: (1) the characteristics of instruction and learning tools, (2) student characteristics, (3) implementation of instruction and learning tools, and (4) the students’ environments. While there has been much research on factor 1 (characteristics of instruction and learning tools), there has been less focus on factor 2 (students’ characteristics, and in particular, motivational factors). Here, we focus on the baseline characteristics of introductory physics students obtained from survey data to inform factor 2 of the framework. A longitudinal analysis of students’ motivational characteristics in two-semester introductory physics courses was performed by administering pre- and post-surveys that evaluated students’ self-efficacy, grit, fascination with physics, value associated with physics, intelligence mindset, and physics epistemology. We found that female students reported lower levels of self-efficacy, fascination, and value associated with physics, and held a more “fixed” view of intelligence in the context of physics compared to male students. Female students’ fascination and value associated with physics decreased significantly more than males’ after an introductory physics course sequence. In addition, females’ view of physics intelligence became more “fixed” compared to males’ by the end of an introductory physics course sequence. Grit was the only factor on which females reported averages that were equal to or higher than males throughout introductory physics courses. The findings inform the framework and have implications for the development and implementation of effective pedagogies and learning tools to help all students learn.

Written quiz responses of 653 students in three separate courses are analyzed in detail. There has been relatively little research on student learning of thermodynamics in physics courses at the university level. A recent study by Loverude et al. has made it evident that students at the introductory level (and beyond) face many significant difficulties in learning fundamental thermodynamic concepts such as the first law of thermodynamics. I have been engaged in an ongoing project with T. J. Greenbowe to investigate student learning of thermodynamics in both physics and chemistry courses. As part of that investigation, a short diagnostic quiz has been administered over the past two years in the calculus-based introductory physics course at Iowa State University (ISU). This quiz focuses on heat, work, and the first law of thermodynamics. At ISU, thermodynamics is studied at the end of the second semester of the twosemester sequence in calculus-based introductory general physics. This course is taught in a traditional manner, with large lecture classes (up to 250 students), weekly recitation sections (about 25 students), and weekly labs taught by graduate students. Homework is assigned and graded every week. Thermal physics comprises 18-20% of the course coverage, and includes a wide variety of topics such as calorimetry, heat conduction, kinetic theory, laws of thermodynamics, heat engines, entropy, etc. The diagnostic quiz used in this study is shown below; it has been administered in three separate classes. The version shown here was administered in May 2001; the other two versions (December 1999 and December 2000) had very minor variations from the one shown here. (There were one or two additional questions on these quizzes which are not discussed here.) The 1999 and 2000 classes were taught by the same instructor, using a different textbook in each course. The 2001 course was taught by a different instructor, using the same text that was employed in the 1999 course. Both instructors are very experienced and have taught introductory physics at ISU for many years. The quiz was administered in two different ways: in 1999 and 2001, it was given as a practice quiz in the final recitation session (last week of class). In almost all cases it was ungraded; one instructor used it as a graded quiz. In 2000 the quiz was administered as an ungraded practice quiz in the very last lecture class of the year. This p-V diagram represents a system consisting of a fixed amount of ideal gas that undergoes two different processes in going from state A to state B:

The development and integration of digital technology, in the current social context, makes it necessary to design and implement educational proposals that contribute to strengthening the teaching-learning processes through resources and didactic materials that provide dynamism, flexibility and innovation. One of the approaches that is gaining popularity in the international scenario is to approach the teaching of technology, programming and other digital skills such as computational thinking from an early age. This paper presents some of the results achieved through the development of a formative experience to foster the learning of computational thinking in early childhood education. The study corresponds to a quasi-experimental design with pretest-postest measures, without control group. The sample of participants was 44 students and 2 teachers, from a concerted school, in Salamanca, Spain, during the period 2017-2018. The activities consisted of solving problems with programming challenges using a tangible and a graphical interface. The data were collected through a rubric, questionnaires and field diary. The general results show the existence of differences between pretest and postest, which indicates that an advance was generated in reference to the learning of computational thinking through the explored characteristic. In addition, there is a positive acceptance of the activities between students and teachers. The study represents an initial assessment of the development of programming and computational thinking skills in young children using tangible and graphical learning interfaces.

Computational thinking skill is an important skill individuals should acquire to meet the requirements of the digital age. The aim of the study is to predict the computational thinking skills of middle school students through ANFIS approach, which is an adaptive neural network-based fuzzy logic. Students' computational thinking skill scores were predicted by creating a model based on grade level and academic achievement variables. Grade level and academic achievement served as the model's input variables, and computational thinking skill scores served as the model's output variable. Data were collected using personal information form and computational thinking scale. A comparison was made between students' real and artificial computational thinking skill scores using statistical methods. In the study, a strong and favorable association between the artificial scores produced using the ANFIS technique and the actual scores was discovered. Furthermore, there was no statistically significant difference between the real and artificial scores for computational thinking skills. These results indicate that the ANFIS approach is a suitable alternative analysis method for predicting students' computational thinking skills. The study provides a good example in the field of education where artificial intelligence can be used to predict students' educational characteristics.

In this paper, we propose the computational thinking education framework which provides three steps of computational thinking process and three kind of activities about computational thinking learning in class. The key idea of this framework is to provide the guidelines of designing activity steps of teaching and learning computational thinking in class using three axles of framework such as problem area, process of learning, and steps of computational thinking process. After designing a framework, we show that sample course of programming education program containing contents of Informatics subject in middle school by implementing our framework. Proposed framework and programming education program in middle school will be the good case study and guide to implement computational thinking concerned education programs in elementary, secondary, and universities.

Dialog with a social pedagogical robot or agent is a powerful way for kids to learn [1, 5] but may limit the formation of an agentic relationship with the technology [9]. One main purpose of conversational agents is to allow the user to have a natural interaction that reduces the need to learn artificial conventions [6], but dialog systems fall short with respect to failure recovery, vocabulary diversity, remembering conversational history, and other measures [2, 3]. Further, Hill et. al. [4] found that people adapt their model of communication to match a chatbot's in the same way they do with a child or non-native speaker. Thus, users conversing with a pedagogical agent are implicitly trained to shape their behavior to suit the technology rather than shaping the technology. For young learners, particularly among populations that have been historically excluded from technology fields, this limits agency and reinforces marginalizing power structures [9].

&lt;span&gt;This study intends to carry out a bibliometric analysis to answer the following research questions: What is the annual increase in publications of constructionism, constructivism, and computational thinking, and mathematics education?; Who are the most prolific writers of constructionism, constructivism, computational thinking, and mathematics education?; Which the most countries in the fields of constructivism, constructivism, computational thinking, and mathematics education?; Who are the most frequently cited scientists? In the end, the biblioAnalysis function generates descriptive data from bibliographic data. Thus, this description has explained that these keywords are sufficient to describe the use of theory in learning computational thinking and mathematics. After we have completed our mapping, we will have a combined framework at our disposal, which will allow us to gain a deeper understanding of the possible connections that exist between constructionism, constructivism, computational thinking, and mathematics education.&lt;/span&gt;

The technological advance that is currently evident in the different social contexts is contributing to consolidate educational processes that allow the strengthening of technological and social skills in students. This paper presents some results obtained in the development of a learning experience in computational thinking and social interaction skills, using problem solving activities and educational robotics in a playful way. The experience involved 46 students and 2 teachers of the first level of primary education of a concerted school in Salamanca, Spain, during the period 2017-2018. A rubric and checklists were used as data collection instruments. The results show a significant advance in the computational thinking and social development skills explored.

Promoting computational thinking is one of the top priorities in CS education as well as in other STEM and non-STEM disciplines. Our innovative NSF-funded IC2Think project blends computational thinking with creative thinking so that students leverage their creative thinking skills to “unlock” their understanding of computational thinking. In Fall 2012, we deployed creative exercises designed to engage Epstein's creative competencies (Surrounding, Capturing, Challenging and Broadening) in introductory level CS courses targeting four different groups (CS, engineering, combined CS/physical sciences, and humanities majors). Students combined hands-on problem solving with guided analysis and reflection to connect their creative activities to CS topics such as conditionals and arrays and to real-world CS applications. Evaluation results (approximately 150 students) found that creative thinking exercise completion had a linear “dosage” effect. As students completed more exercises [0/1 - 4], they increased their long-term retention [a computational thinking test], F(3, 98) = 4.76, p =.004, partial Eta2 = .127 and course grades, F(3, 109) = 4.32, p =.006, partial Eta2 = .106. These findings support our belief that the addition of creative thinking exercises to CSCE courses improves the learning of computational knowledge and skills.

This document presents the results of the development of a learning program on computational thinking through programming challenges with robots oriented to early childhood. The methodological approach used was quantitative, using a quasi-experimental design, taking pretest/posttest measures, with experimental and control groups. The sample of participating students was 46. They belonged to a group of first grade of primary education, aged between 6 and 7 years, in a Spanish educational center. The learning of computational thinking was measured through the following dimensions: algorithmic thinking-sequences, abstraction-patterns and debugging. The learning activities used were an adaptation of the activities proposed in the “TangibleK” robotics curriculum. The results generated show positive effects in relation to the level of achievement reached by the students in the proposed challenges; in other words, there is a significant effect on the mastery of skills related to computational thinking. Differences were found between the pretest and posttest measures of the experimental group. Those of the latter group were statistically significant and higher than those of the control group. Therefore, we can statistically attribute the mastery of computational thinking skills to the participation in the training activities.

This study aims to find out whether there is an influence of the Problem Based Learning-Computational Thinking (PBL-CT) model on analytical thinking skills in terms of students' interest in Natural Sciences subjects. This research was conducted at the Kalam Kudus Christian Middle School in Malang in the 2022/2023 academic year. The design used in this study was a non-equivalent group design quasi-experiment. Retrieval of data using ten essay questions which are made according to indicators of analytical thinking ability and students' interest in science is measured by using a questionnaire. Data analysis was carried out by the Two Way Anova parametric test. Based on the results of the analysis carried out, the results were obtained: (1) there were differences in analytical thinking skills between students who were taught using the Problem Based Learning and Problem Based Learning - Computational Thinking (PBL-CT) learning models with a significance &lt;0.05. (2) there is no difference in the ability to think analytically between students who have a high, medium, and low interest in learning science as shown by a significance value of 0.748 &gt; 0.05. (3) there is an interaction between Problem Based Learning - Computational Thinking (PBL-CT) on students' analytical thinking skills in terms of students' interest in natural science subjects with a significance value of &lt;0.05

To help novice learners overcome the obstacles of learning computational thinking (CT) through programming, it is vital to identify difficult CT components. This study aimed to determine the computational concepts and practices that learners may have difficulties acquiring and discuss how programming instructions should be designed to facilitate learning CT in online learning environments. Participants included 92 undergraduate students enrolled in an online course. Data were collected from a CT knowledge test and coding journals. Results revealed that four computational concepts (i.e., parallelism, conditionals, data, and operators) and two computational practices (i.e., testing and debugging and abstracting and modularizing) were identified as CT components that were difficult to learn. The findings of this study imply that CT instructions should offer additional instructional supports to enhance the mastery of difficult computational concepts and practices. Further research is necessary to investigate instructional approaches to successful CT learning.