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Prediction versus production for teaching computer programming
Abstract
Background: Most students struggle when learning to program. Aims: In this paper we examine two instructional tasks that can be used to introduce programming: tell-and-practice (the typical pedagogical routine of describing some code or function then having students write code to practice what they have learned) and prediction (where students are given code and asked to make predictions about the output before they are told how the code works). Sample: Participants were 121 college students with no coding experience. Methods: Participants were randomly assigned to one of two parallel training tasks: predict, or tell-and-practice. Results: Participants in the predict condition showed greater learning and better non-cognitive outcomes than those in the tell-and-practice condition. Conclusions: These findings raise a number of questions about the relationship between programming tasks and students' experiences and outcomes in the early stages of learning programming. They also suggest some pedagogical changes to consider, especially in early introductions to programming.
... Second, experts have extensive experience writing and running many lines of code; this enables them to infer causal relations between code and its output (Holyoak & Cheng, 2011). This knowledge of cause-effect relationships between the code and its output allows them to predict output (Tucker et al., 2024) and can be used to problem solve with code (Iwamoto et al., 2020). Novice programmers who do not have such accumulated experience struggle to predict code output (Lister et al., 2004) or to identify the code examples that match their goals (Iwamoto et al., 2020;Kashima, 2019). ...
Although programming is often learned through formal instruction or self-paced tutorials, informal learning, for example, through publicly available online documentation, is also a significant resource for skill development among novices. However, many novices struggle to extract useful information from documentation. This work aims to answer two key questions: (1) What specific elements of documentation make it difficult for novice learners? (2) What are some design features that could enhance the effectiveness of documentation for novices? Study 1 aims to qualitatively explore the impact of three hypothesized design changes on novice programmers’ learning experience: moving examples to the top of the documentation, ordering examples in increasing argument complexity, and displaying example outputs next to example codes. We propose these design changes should facilitate novices’ learning from the documentation, enhancing its accessibility and effectiveness. Studies 2 and 3 evaluate the effectiveness of the redesigned documentation on novices’ learning outcomes through a quantitative method. Our results demonstrate that these design changes help novices navigate and apply documentation more effectively.
This chapter provides a comprehensive review and analysis of recent advancements in both theory and practice concerning programming language learning, specifically within the K-12 educational environment. We synthesize the relevant research literature across the domains of reading, writing, and emerging work integrating both perspectives. Evidence-based pedagogical approaches are reviewed alongside the instructional capabilities offered by educational technology. The chapter concludes by reflecting on the challenges and opportunities of generative artificial intelligence tools. These insights are intended to inform educators and researchers with an interest in skill development within the domain of computer science education, offering practical recommendations for the design of effective instruction to support novice programmers.
Testing students on information that they do not know might seem like a fruitless endeavor. After all, why give anyone a test that they are guaranteed to fail because they have not yet learned the material? Remarkably, a growing body of research indicates that such testing—formally known as prequestioning or pretesting—can benefit learning if there is an opportunity to study the correct answers afterwards. This prequestioning effect or pretesting effect has been successfully demonstrated with a variety of learning materials, despite many erroneous responses being generated on initial tests, and in conjunction with text materials, videos, lectures, and/or correct answer feedback. In this review, we summarize the emerging evidence for prequestioning and pretesting effects on memory and transfer of learning. Uses of pre-instruction testing in the classroom, theoretical explanations, and other considerations are addressed. The evidence to date indicates that prequestioning and pretesting can often enhance learning, but the extent of that enhancement may vary due to differences in procedure or how learning is assessed. The underlying cognitive mechanisms, which can be represented by a three-stage framework, appear to involve test-induced changes in subsequent learning behaviors and possibly other processes. Further research is needed to clarify moderating factors, theoretical issues, and best practices for educational applications.
The paper presents a model for classroom activities where true or false and multiple-choice questions are approached in an innovative way. Instead of simply providing answers to the questions, students are required to place a bet on their preferred answer, starting from a predetermined set of points, which change on each subsequent iteration depending on the correctness of the answers to the previous questions. Students are divided in teams and make the decision about the bet together, competing against other teams. We describe the design of a template spreadsheet for the conducting of the activities and briefly discuss the advantages and disadvantages of the model.
Misconceptions about scientific concepts often prevail even if learners are confronted with conflicting evidence. This study tested the facilitative role of surprise in children’s revision of misconceptions regarding water displacement in a sample of German children (N = 94, aged 6–9 years, 46% female). Surprise was measured via the pupil dilation response. It was induced by letting children generate predictions before presenting them with outcomes that conflicted with their misconception. Compared to a control condition, generating predictions boosted children’s surprise and led to a greater revision of misconceptions (d = 0.56). Surprise further predicted successful belief revision during the learning phase. These results suggest that surprise increases the salience of a cognitive conflict, thereby facilitating the revision of misconceptions.
This article attempts to delineate the procedural and mechanistic characteristics of predicting as a learning strategy. While asking students to generate a prediction before presenting the correct answer has long been a popular learning strategy, the exact mechanisms by which it improves learning are only beginning to be unraveled. Moreover, predicting shares many features with other retrieval-based learning strategies (e.g., practice testing, pretesting, guessing), which begs the question of whether there is more to it than getting students to engage in active retrieval. I argue that active retrieval as such does not suffice to explain beneficial effects of predicting. Rather, the effectiveness of predicting is also linked to changes in the way the ensuing feedback is processed. Initial evidence suggests that predicting boosts surprise about unexpected answers, which leads to enhanced attention to the correct answer and strengthens its encoding. I propose that it is this affective aspect of predicting that sets it apart from other retrieval-based learning strategies, particularly from guessing. Predicting should thus be considered as a learning strategy in its own right. Studying its unique effects on student learning promises to bring together research on formal models of learning from prediction error, epistemic emotions, and instructional design.
Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture. Our knowledge of many of the characteristics of working memory, long-term memory and the relations between them had been well-established for many decades prior to the introduction of the theory. Curiously, this knowledge had had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist. In contrast, cognitive load theory emphasised that all novel information first is processed by a capacity and duration limited working memory and then stored in an unlimited long-term memory for later use. Once information is stored in long-term memory, the capacity and duration limits of working memory disappear transforming our ability to function. By the late 1990s, sufficient data had been collected using the theory to warrant an extended analysis resulting in the publication of Sweller et al. (Educational Psychology Review, 10, 251–296, 1998). Extensive further theoretical and empirical work have been carried out since that time and this paper is an attempt to summarise the last 20 years of cognitive load theory and to sketch directions for future research.
Background and Context: Current introductory instruction fails to identify, structure, and sequence the many skills involved in programming.
Objective: We proposed a theory which identifies four distinct skills that novices learn incrementally. These skills are tracing, writing syntax, comprehending templates (reusable abstractions of programming knowledge), and writing code with templates. We theorized that explicit instruction of these skills decreases cognitive demand.
Method: We conducted an exploratory mixed-methods study and compared students’ exercise completion rates, error rates, ability to explain code, and engagement when learning to program. We compared material that reflects this theory to more traditional material that does not distinguish between skills.
Findings: Teaching skills incrementally resulted in improved completion rate on practice exercises, and decreased error rate and improved understanding of the post-test.
Implications: By structuring programming skills such that they can be taught explicitly and incrementally, we can inform instructional design and improve future research on understanding how novice programmers develop understanding.
Efforts to improve computer science education are underway, and teachers of computer science are challenged in introductory programming courses to help learners develop their understanding of programming and computer science. Identifying and addressing students’ misconceptions is a key part of a computer science teacher's competence. However, relevant research on this topic is not as fully developed in the computer science education field as it is in mathematics and science education. In this article, we first review relevant literature on general definitions of misconceptions and studies about students’ misconceptions and other difficulties in introductory programming. Next, we investigate the factors that contribute to the difficulties. Finally, strategies and tools to address difficulties including misconceptions are discussed.
Based on the review of literature, we found that students exhibit various misconceptions and other difficulties in syntactic knowledge, conceptual knowledge, and strategic knowledge. These difficulties experienced by students are related to many factors including unfamiliarity of syntax, natural language, math knowledge, inaccurate mental models, lack of strategies, programming environments, and teachers’ knowledge and instruction. However, many sources of students’ difficulties have connections with students’ prior knowledge. To better understand and address students’ misconceptions and other difficulties, various instructional approaches and tools have been developed. Nevertheless, the dissemination of these approaches and tools has been limited. Thus, first, we suggest enhancing the dissemination of existing tools and approaches and investigating their long-term effects. Second, we recommend that computing education research move beyond documenting misconceptions to address the development of students’ (mis)conceptions by integrating conceptual change theories. Third, we believe that developing and enhancing instructors’ pedagogical content knowledge (PCK), including their knowledge of students’ misconceptions and ability to apply effective instructional approaches and tools to address students’ difficulties, is vital to the success of teaching introductory programming.
The aim of this study is to teach enzymes, which are one of the biology subjects in understanding which students have a big difficulty, to pre-service teachers through POE method in the case of catalase, which is an oxidoreductase. Descriptive analysis method was employed in this study in which 38 second grade pre-service teachers attending Uludag University Faculty of Education Department of Science Teaching in the spring semester of the 2014-2015 academic year and taking the "General Biology Laboratory" course participated. Four worksheets based on the POE method were used for data collection. The titles of these worksheets are "The Effect of Catalase", "Catalase Concentration", "Relationship between Catalase Concentration and Surface Increases in a Liver Piece", and "Can Catalase Be Re-used?". Frequency (f) and percentage (%) values were taken into consideration in data analysis. Certain results were obtained in the present study in regard to the effect of catalase on H2O2(hydrogen peroxide), catalase concentration, and enzyme-substrate relationship and enzyme-surface area relationship. The averages calculated based on the answers given by the students to the questions asked to them in relevant stages indicate that success rate was 88.58% in the predict stage, 61.18% in the observe stage, and 87.72% in the explain stage while the overall success rate of the POE method was 79.16% for the activity entitled, "The Effect of Catalase"; 64.25% in the predict stage, 75.75% in the observe stage, and 81.58% in the explain stage while the overall success rate of the POE method was 73.86% for the activity entitled, "Catalase Concentration"; 93.86% in the predict stage, 98.68% in the observe stage, and 97.37% in the explain stage while the overall success rate of the POE method was 96.64% for the activity entitled, "Relationship between Catalase Concentration and Surface Increases in a Liver Piece"; and 83.55% in the predict stage, 88.6% in the observe stage, and 87.5% in the explain stage while the overall success rate of the POE method was 86.55% for the activity entitled "Can Catalase Be Re-used?
Previous investigations of student errors have typically focused on samples of hundreds of students at individual institutions. This work uses a year's worth of compilation events from over 250,000 students all over the world, taken from the large Blackbox data set. We analyze the frequency, time-to-fix, and spread of errors among users, showing how these factors inter-relate, in addition to their development over the course of the year. These results can inform the design of courses, textbooks and also tools to target the most frequent (or hardest to fix) errors.
Roles of variables capture the dynamic nature of variables, i.e., their behavior. Only ten roles are needed to cover 99 % of variables in novice-level procedural programs. Roles were originally identified by studying variables in existing programs and creating a classification for them. In order to find out whether roles are a part of experts ’ programming knowledge, we conducted a knowledge elicitation investigation where professional programmers studied programs and the resulting mental representations were elicited using card sorting and interviews. This paper presents the analysis of the results from the point of view of the role theory. All roles appearing in the materials were identified by participants. There was some variation in perceiving the nature of behavior from the lifetime of a variable and in considering the similarity of behaviors. The roles could however be easily found in the participants ’ card sorting results and in the dendrogram obtained by hierarchical cluster analysis. 1
A student's capacity to learn a concept is directly related to how much cognitive load is used to comprehend the material. The central problem identified by Cognitive Load Theory is that learning is impaired when the total amount of processing requirements exceeds the limited capacity of working memory. Instruction can impose three different types of cognitive load on a student's working memory: intrinsic load, extraneous load, and germane load. Since working memory is a fixed size, instructional material should be designed to minimize the extraneous and intrinsic loads in order to increase the amount of memory available for the germane load. This will improve learning. To effectively design instruction to minimize cognitive load we must be able to measure the specific load components for any pedagogical intervention. This paper reports on a study that adapts a previously developed instrument to measure cognitive load. We report on the adaptation of the instrument to a new discipline, introductory computer science, and the results of measuring the cognitive load factors of specific lectures. We discuss the implications for the ability to measure specific cognitive load components and use of the tool in future studies.
Although numerous theories of motivation have been proposed over the past few decades, Expectancy-Value models of motivation stand out for their ability to synthesize multiple theoretical perspectives, capture the key components of what motivates an individual, and explain a wide range of achievement-related behaviors. In the current chapter, we review the contemporary perspective of Expectancy-Value models used in education. As its name suggests, Expectancy-Value models have centered on the importance of two components in promoting overall motivation: having an expectancy of being successful in a task and having a value for engaging in the task. In addition, as indicated by the title of our chapter, we propose promoting a third component into the overall name of the model to highlight the additional role that the cost of engaging in an activity plays in influencing motivation. Although cost is theorized to be a sub-component in prior Expectancy-Value models, it has been largely ignored in past empirical work (Wigfield & Cambia, 2010). Fortunately, cost has re-emerged with recent research demonstrating its importance in capturing motivational dynamics that complement expectancy and value components.
Taking a test before study can improve subsequent learning of that pretested information. How the pretest affects subsequent learning of other information in the passage is less clear, however. In three experiments, we examined the consequences of taking a multiple-choice (MC) pretest on the later recall of both pretested and non-pretested related information, finding that pretesting improved recall of pretested information without impairing recall of non-pretested information. In addition, we compared a pretest condition to conditions in which subjects were told to memorize the questions and in which subjects studied facts prior to reading. Although taking a pretest was not significantly more effective than memorizing questions or studying facts for the pre-exposed information, it did not impair the learning of related information, whereas studying facts did. Thus, even when an MC pretest takes time away from study, that pretest appears to make subsequent study more effective than other types of activities that pre-expose students to to-be-tested information.
Previous research suggests that students' prior knowledge can interfere with how they observe and remember lecture demonstrations. We measured students' prior knowledge in introductory mechanics and electricity and magnetism at two large universities. Students were then asked to predict the outcome of lecture demonstrations. We compare students' predictions before having seen the demonstration to what they report having seen both right after the demonstration and several weeks later. We report four main findings. First, roughly one out of every five observations of a demonstration is inconsistent with the actual outcome. Second, students who understand the underlying concepts before observing the demonstration are more likely to observe it and remember it correctly. Third, students are roughly 20% (23%) more likely to observe a demonstration correctly if they predict the outcome first, regardless of whether the prediction is correct or not. Last, conceptual learning is contingent on the student making a correct observation. This study represents an initial step towards understanding the disconnect reported between demonstrations and student learning.
In this paper, the Block Model, an educational model of program comprehension, is introduced. Its use for planning and analyzing lessons on algorithms is evaluated in a qualitative study with prospective computer science teachers. In addition, the background of the model, its use in computer science education research and for developing competence models is discussed.
Activities that promote student invention can appear inefficient, because students do not generate canonical solutions, and therefore the students may perform badly on standard assessments. Two studies on teaching descriptive statistics to 9th-grade stu- dents examined whether invention activities may prepare students to learn. Study 1 found that invention activities, when coupled with subsequent learning resources like lectures, led to strong gains in procedural skills, insight into formulas, and abilities to evaluate data from an argument. Additionally, an embedded assessment experiment crossed the factors of instructional method by type of transfer test, with 1 test includ- ing resources for learning and 1 not. A "tell-and-practice" instructional condition led to the same transfer results as an invention condition when there was no learning re- source, but the invention condition did better than the tell-and-practice condition when there was a learning resource. This demonstrates the value of invention activi- ties for future learning from resources, and the value of assessments that include op- portunities to learn during a test. In study 2, classroom teachers implemented the in- struction and replicated the results. The studies demonstrate that intuitively compelling student-centered activities can be both pedagogically tractable and effec- tive at preparing students to learn.
We report on a longitudinal research study of the development of novice programmers in their first semester of programming. In the third week, almost half of our sample of students could not answer an explain-in-plain-English question, for code consisting of just three assignment statements, which swapped the values in two variables. We regard code that swaps the values of two variables as the simplest case of where a programming student can manifest a SOLO relational response. Our results demonstrate that the problems many students face with understanding code can begin very early, on relatively trivial code. However, using traditional programming exercises, these problems often go undetected until late in the semester. New approaches are required to detect and fix these problems earlier.
Eighteen year-11 physics students were taught heat and expansion of liquids using a predict-observe-explain sequence. Argues that the data demonstrate how students' prior knowledge and beliefs can affect their observations and interpretations of new learning. (Author/MKR)
Being told procedures and concepts before problem solving can inadvertently undermine the learning of deep structures in physics. If students do not learn the underlying structure of physical phenomena, they will exhibit poor transfer. Two studies on teaching physics to adolescents compared the effects of “telling” students before and after problem solving. In Experiment 1 (N = 128), students in a tell-and-practice condition were told the relevant concepts and formulas (e.g., density) before practicing on a set of contrasting cases for each lesson. Students in an invent-with-contrasting-cases (ICC) condition had to invent formulas using the same cases and were told only afterward. Both groups exhibited equal proficiency at using the formulas on word problems. However, ICC students better learned the ratio structure of the physical phenomena and transferred more frequently to semantically unrelated topics that also had a ratio structure (e.g., spring constant). Experiment 2 (N = 120) clarified the sources of the effects while showing that ICC benefited both low- and high-achieving students. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This study analyzed student responses to an examination, after the students had completed one semester of instruction in programming. The performance of students on code tracing tasks correlated with their performance on code writing tasks. A correlation was also found between performance on "explain in plain English" tasks and code writing. A stepwise regression, with performance on code writing as the dependent variable, was used to construct a path diagram. The diagram suggests the possibility of a hierarchy of programming related tasks. Knowledge of programming constructs forms the bottom of the hierarchy, with "explain in English", Parson's puzzles, and the tracing of iterative code forming one or more intermediate levels in the hierarchy.
In this paper we report on a multi-institutional investigation into the reading and comprehension skills of novice programmers. This work extends previous studies (Lister 2004, McCracken 2001) by developing a question set within two key pedagogical frameworks: the Bloom and SOLO taxonomies. From this framework of analysis some interesting emergent patterns relating the cognitive level of the questions to student performance have been identified.
This study was carried out in order to investigate the impact of implementing video-based predict-observe-explain (POE) on students’ scientific literacy, particularly in chemistry’s concept of salts. The hypothesis of this study was that students’ scientific literacy would improve after implementing video-based POE within teaching, in the context of preparation of salts. In addition, it was hypothesized that students who had a positive attitude towards learning with the aid of videos would perform better than students who had a neutral or negative attitude. Thus, the two research questions were: (1) Does video-based POE improve year 9 students’ scientific literacy on salts? (2) How do students’ views of video-based POE affect their learning? The students in this study underwent two intervention cycles. The research design employed was mixed methods action research, with qualitative data collected via students’ semi-structured interviews and intervention worksheets, which were thematically analysed, and quantitative data from their pre- and posttests scores analysed using Wilcoxon ranks and Mann Whitney U test in SPSS. The results indicated students’ scientific literacy on salts had improved post-intervention, and their metacognition enhanced irrespective of their views. Students’ misconceptions of salts were also identified, thus implicating video-based POE as an effective constructivist learning approach that enhances metacognition and hence, scientific literacy.
While research has shown that students’ epistemic beliefs can be a strong predictor of their academic performance, cognitive abilities, or self-efficacy, studies of this topic in computer education are rare. The purpose of this study was twofold. First, it aimed to validate a newly developed questionnaire for measuring students’ epistemic beliefs about computer programming at middle school level. Second, the study aimed to understand how students’ epistemic beliefs predict their computational thinking disposition and computer programming self-efficacy. A total of 406 middle school students completed a survey consisting of three research instruments: the Computer Programming Epistemic Belief Inventory (CPEBI), the Computational Thinking Scale (CTS), and the Computer Programming Self-Efficacy Scale (CPSES). The results indicated that the epistemic belief of “uncertainty” was a significant and positive predictor of the “abstraction” and “generalization” dimensions of computational thinking dispositions. In addition, the belief of “complexity” was an antecedent to three dimensions of computational thinking, namely, “algorithmic thinking,” “evaluation,” and “generalization.” Only the “evaluation” dimension of CT disposition had significant and positive relationships with “programming self-efficacy.” The roles of “uncertainty” and “complexity” of computer programming epistemic beliefs are discussed. Implications and suggestions for future computer programming education, especially for young students, are provided.
Critical thinking is embedded in 2 dimensions, critical attitude and ability, which may or may not mutually affect students’ self-confidence in science inquiry learning. To understand this issue, we designed a website called Enriched Thinking Cloud based on the predict-observe-explain (POE) model of inquiry-based learning by providing themes for students to learn about moon science concepts. Moreover, we calculated their critical performance based on object identification theory. Data collected from 163 participants were subjected to confirmatory factor analysis with structural equation modeling to test the prediction among paths of the research model. The results showed that attitude toward POE online inquiry learning can positively predict participants’ self-confidence in using Enriched Thinking Cloud (hereafter “self-confidence”) and can also predict their critical attitude. Furthermore, self-confidence and critical attitude can positively predict their cognitive dimension of the critical thinking process. The results suggest that science teachers can design more POE inquiry models to enhance students’ critical ability and attitude in earth science learning.
Using both behavioral and eye-tracking methodology, we tested whether and how asking students to generate predictions is an efficient technique to improve learning. In particular, we designed two tasks to test whether the surprise induced by outcomes that violate expectations enhances learning. Data from the first task revealed that asking participants to generate predictions, as compared to making post hoc evaluations, facilitated acquisition of geography knowledge. Pupillometry measurements revealed that expectancy-violating outcomes led to a surprise response only when a prediction was made beforehand, and that the strength of this response was positively related to the amount of learning. Data from the second task demonstrated that making predictions about the outcomes of soccer matches specifically improved memory for expectancy-violating events. These results suggest that a specific benefit of making predictions in learning contexts is that it creates the opportunity for the learner to be surprised. Implications for theory and educational practice are discussed.
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This paper describes an investigation into the viability of mental models used by novice programmers at the end of a first year Java programming course. The qualitative findings identify the range of mental models of value and reference assignment held by the participants. The quantitative analysis reveals that approximately one third of students held non-viable mental models of value assignment and only 17% of students held a viable mental model of reference assignment. Further, in terms of a comparison between the participants' mental models and their performance in in-course assessments and final examination, it was found that students with viable mental models performed significantly better than those with non-viable models. These findings are used to propose a more "constructivist" approach to teaching programming based on the integration of "cognitive conflict" and program visualisation.
Although the testing effect has received a substantial amount of empirical attention, such research has largely focused on the effects of tests given after study. The present research examines the effect of using tests prior to study (i.e., as pretests), focusing particularly on how pretesting influences the subsequent learning of information that is not itself pretested but that is related to the pretested information. In Experiment 1, we found that multiple-choice pretesting was better for the learning of such related information than was cued-recall pretesting or a pre-fact-study control condition. In Experiment 2, we found that the increased learning of non-pretested related information following multiple-choice testing could not be attributed to increased time allocated to that information during subsequent study. Last, in Experiment 3, we showed that the benefits of multiple-choice pretesting over cued-recall pretesting for the learning of related information persist over 48 hours, thus demonstrating the promise of multiple-choice pretesting to potentiate learning in educational contexts. A possible explanation for the observed benefits of multiple-choice pretesting for enhancing the effectiveness with which related nontested information is learned during subsequent study is discussed.
We built detectors capable of automatically recognizing affective states of novice computer programmers from student-annotated videos of their faces recorded during an introductory programming tutoring session. We used the Computer Expression Recognition Toolbox (CERT) to track facial features based on the Facial Action Coding System, and machine learning techniques to build classification models. Confusion/Uncertainty and Frustration were distinguished from all other affective states in a student-independent fashion at levels above chance (Cohen’s kappa = .22 and .23, respectively), but detection accuracies for Boredom, Flow/Engagement, and Neutral were lower (kappas = .04, .11, and .07). We discuss the differences between detection of spontaneous versus fixed (polled) judgments as well as the features used in the models.
The aim of this study was to explore Taiwanese college students’ conceptions of and approaches to learning computer science and then explore the relationships between the two. Two surveys, Conceptions of Learning Computer Science (COLCS) and Approaches to Learning Computer Science (ALCS), were administered to 421 college students majoring in computer science-related departments in Taiwan. The COLCS survey included the following seven factors, in a hierarchical order: learning computer science as “Memorizing,” “Testing,” “Calculating and practicing,” “Programming,” “Increasing one’s knowledge,” “Application and understanding,” and “Seeing in a new way.” Particularly, differing from previous learning conception studies, one newly developed factor, “Programming,” was added to COLCS to incorporate this unique feature for computer science students. The ALCS survey consisted of four factors: “Surface motive,” “Surface strategy,” “Deep motive,” and “Deep strategy.” The results showed that these two surveys were deemed to be sufficiently reliable for assessing students’ conceptions of and approaches to learning computer science. It was also found that the “Programming” factor should be considered as a higher-level learning conception, similar to the other higher-level conceptions in COLCS such as “Increase one’s knowledge,” “Application and understanding,” and “Seeing in a new way.” Furthermore, the “Application” and “Understanding” factors were merged into a single factor, “Application and understanding,” in this study. However, it was indicated that both Taiwanese college students’ lower- and higher-level learning conceptions were positively correlated to their surface motivations to learn computer science.
Monique Boekaerts posits that the role of emotions and motivations has been seriously neglected in the design of learning arrangements and teacher professional development. She summarises knowledge about the key role of emotions and motivations around a small number of principles. Students are more motivated to engage in learning when: they feel competent to do what is expected of them and perceive stable links between actions and achievement; they value the subject and have a clear sense of purpose; they experience positive emotions towards learning activities and, contrariwise, turn away from learning when they experience negative emotions; and when they perceive the environment as favourable for learning. Students free up cognitive resources when they are able to influence the intensity, duration and expression of their emotions, and are more persistent in learning when they can manage their resources and deal with obstacles efficiently.
Cognitive Load Theory (CLT) began as an instructional theory based on our knowledge of human cognitive architecture. It proved successful in generating a series of cognitive load effects derived from the results of randomised, controlled experiments (Clark, Nguyen, & Sweller, 2006). This chapter summarises the theory, including its general instructional implications. Many of the theory's specific instructional implications, which provide its prime function and purpose, are discussed in other chapters in this volume and therefore will not be discussed in detail in this chapter (see Table 2.1 for a summary). HUMAN COGNITION The processes of human cognition constitute a natural information-processing system that mimics the system that gave rise to human cognitive architecture: evolution by natural selection. Both human cognition and biological evolution create novel information, store it for subsequent use, and are capable of disseminating that information indefinitely over space and time. By considering human cognition within an evolutionary framework, our understanding of the structures and functions of our cognitive architecture are being transformed. In turn, that cognitive architecture has profound instructional consequences. CLT is an amalgam of human cognitive architecture and the instructional consequences that flow from that architecture. From an evolutionary perspective, there are two categories of human knowledge: biologically primary and biologically secondary knowledge (Geary, 2007, 2008). Biologically primary knowledge is knowledge we have evolved to acquire over many generations. Examples are general problem-solving techniques, recognising faces, engaging in social relations, and listening to and speaking our native language.
This paper reports on the authors use of the SOLO taxonomy to describe differences in the way students and educators solve small code reading exercises. SOLO is a general educational taxonomy, and has not previously been applied to the study of how novice programmers manifest their understanding of code. Data was collected in the form of written and think-aloud responses from students (novices) and educators (experts), using exam questions. During analysis, the responses were mapped to the different levels of the SOLO taxonomy. From think-aloud responses, the authors found that educators tended to manifest a SOLO relational response on small reading problems, whereas students tended to manifest a multistructural response. These results are consistent with the literature on the psychology of programming, but the work in this paper extends on these findings by analyzing the design of exam questions.
Suggestions for improving text understanding often prescribe activating prior knowledge, a prescription that may be problematic if students do not have the relevant prior knowledge to begin with. In this article, we describe research about a method for developing prior knowledge that prepares students to learn from a text or lecture. We propose prior knowledge that prepares students to learn from a text or lecture. We propose that analyzing contrasting cases can help learners generate the differentiated knowledge structures that enable them to understand a text deeply. Noticing the distinctions between contrasting cases creates a "time for telling"; learners are prepared to be told the significance of the distinctions they have discovered. In 3 classroom studies, college students analyzed contrasting cases that consisted of simplified experimental designs and data from classic psychology experiments. They then received a lecture or text on the psychological phenomena highlighted in the experiments. Approximately 1 week later, the students predicted outcomes for a hypothetical experiment that could be interpreted in light of the concepts they studied. Generating the distinctions between contrasting cases and then reading a text or hearing a lecture led to more accurate predictions than the control treatments of (a) reading about the distinctions between the cases and hearing a lecture, (b) summarizing a relevant text and hearing a lecture, and (c) analyzing the contrasting cases twice without receiving a lecture. We argue that analyzing the contrasting cases increased students' abilities to discern specific features that differentiated classes of psychological phenomena, much as a botanist can distinguish subspecies of a given flower. This differentiated knowledge prepared the students to understand deeply an explanation of the relevant psychological principles when it was presented to them. These results can inform constructivist models of instruction as they apply to classroom activities and learning from verbal materials. In particular, the results indicate that there is a place for lectures and readings in the classroom if students have sufficiently differentiated domain knowledge to use the expository materials in a generative manner.
Visual program simulation (VPS) is a form of interactive program visualization in which novice programmers practice tracing computer programs: using a graphical interface, they are expected to correctly indicate each consecutive stage in the execution of a given program. Naturally, students make mistakes during VPS; in this article, we report a study of such mistakes.
Visual program simulation tries to get students to act on their conceptions; a VPS-supporting software system may be built so that it reacts to student behaviors and provides feedback tailored to address suspected misconceptions. To focus our efforts in developing the feedback given by our VPS system, UUhistle, we wished to identify the most common mistakes that students make and to explore the reasons behind them. We analyzed the mistakes in over 24,000 student-submitted solutions to VPS assignments collected over three years. 26 mistakes stood out as relatively common and therefore worthy of particular attention. Some of the mistakes appear to be related to usability issues and others to known misconceptions about programming concepts; others still suggest previously unreported conceptual difficulties. Beyond helping us develop our visualization tool, our study lends tentative support to the claim that many VPS mistakes are linked to programming misconceptions and VPS logs can be a useful data source for studying students' understandings of CS1 content.
We propose a model to explain the dynamics of affective states that emerge during deep learning activities. The model predicts that learners in a state of engagement/flow will experience cognitive disequilibrium and confusion when they face contradictions, incongruities, anomalies, obstacles to goals, and other impasses. Learners revert into the engaged/flow state if equilibrium is restored through thought, reflection, and problem solving. However, failure to restore equilibrium as well as obstacles that block goals trigger frustration, which, if unresolved, will eventually lead to boredom. The major hypotheses of the model were supported in two studies in which participants completed a 32–35min tutoring session with a computer tutor. Their affective states were tracked at approximately 110 points in their tutoring sessions via a retrospective affect judgment protocol. Time series analyses confirmed the presence of confusion–engagement/flow, boredom–frustration, and confusion–frustration oscillations. We discuss enhancements of the model to address individual differences and pedagogical and motivational strategies that are inspired by the model.
This article uses time-sampling data to evaluate fifth to ninth graders' experiences of boredom both as a state, related to specific school and nonschool activities, and as a trait, related to individual dispositions that students bring to school. Data come from a study in which 392 youths carried pagers and reported on their activities and emotions at random times over a week when signaled. Results show that, while boredom is reported frequently during schoolwork, it is also prevalent outside school and the same persons report boredom across these contexts. High rates of boredom were correlated with high ability and, when ability is controlled, with oppositional behavior, but not with onset of adolescence. These findings suggest that individual dispositions are an important contributor to boredom. Nonetheless, variations in rates of boredom across school task situations suggest that schools might be structured to reduce, though not eliminate, student boredom.
Learning from worked examples is an effective learning method in well-structured domains. Can its effectiveness be further enhanced when errors are included? This was tested by determining whether a combination of correct and incorrect solutions in worked examples enhances learning outcomes in comparison to correct solutions only, and whether a mixture of correct and incorrect solutions is more effective when the errors are highlighted. In addition, the effectiveness of fostering self-explanations was assessed. In Experiment 1, the participants learned to solve probability problems under six conditions that constituted a 2×3-factorial design (Factor 1: correct and incorrect solutions with highlighting the errors vs. correct and incorrect solutions without highlighting the errors vs. correct solutions only; Factor 2: prompting written self-explanations vs. no prompts). An aptitude-treatment interaction was found: providing correct and incorrect solutions fostered far transfer performance if learners had favourable prior knowledge; if learners had poor prior knowledge correct solutions only were more favourable. Experiment 2 replicated this interaction effect. Thus, a mixture of correct and incorrect solutions in worked examples enhanced learning outcomes only for “good” learners. In addition, Experiment 2 showed that confronting learners with incorrect solutions changed the quality of their self-explanations: on the one hand, new types of effective self-explanations could be observed, but on the other hand the amount of the very important principle-based self-explanations was substantially reduced. A possible measure to prevent this negative side effect of incorrect solutions is discussed.
The teaching of introductory computer programming seems far from successful, with many first-year students performing more poorly than expected. One possible reason for this is that novices hold ‘non-viable’ mental models (internal explanations of how something works) of key programming concepts which then cause misconceptions and difficulties. An initial study investigated the apparent viability of novices' models of fundamental programming concepts, focusing on value and reference assignment. This revealed that many students appeared to hold ‘non-viable’ mental models of these key concepts and that those students who appeared to hold viable mental models performed significantly better in programming tasks than those with non-viable models. To address this, a teaching model integrating cognitive conflict and program visualisation is proposed. A series of studies found that this teaching model is potentially effective in enhancing engagement with learning materials and may therefore help novice programmers develop a better understanding of key concepts.
This article demonstrates the feasibility and effectiveness of teaching several mathematical skills by presenting students with carefully chosen sequences of worked-out examples and problems - without lectures or other direct instruction. Thinking-aloud protocols of 20 students learning factorization by this method are analyzed to determine the kinds and depth of understanding students attained. Students did not simply memorize procedures but were able to recognize when the procedures were applicable and to apply them. Most students were also able to use their understanding of the concept of factorization to help learn the procedures and to check their results. The method of learning from examples has now been tested successfully with a class covering the entire 3-year curriculum in algebra and geometry in a Chinese middle school.
The knowledge required to solve algebra manipulation problems and procedures designed to hasten knowledge acquisition were studied in a series of five experiments. It was hypothesized that, as occurs in other domains, algebra problem-solving skill requires a large number of schemas and that schema acquisition is retarded by conventional problem-solving search techniques. Experiment 1, using Year 9, Year 11, and university mathematics students, found that the more experienced students had a better cognitive representation of algebraic equations than less experienced students as measured by their ability to (a) recall equations, and (b) distinguish between perceptually similar equations on the basis of solution mode. Experiments 2 through 5 studied the use of worked examples as a means of facilitating the acquisition of knowledge needed for effective problem solving. It was found that not only did worked examples, as expected, require considerably less time to process than conventional problems, but that subsequent problems similar to the initial ones also were solved more rapidly. Furthermore, decreased solution time was accompanied by a decrease in the number of mathematical errors. Both of these findings were specific to problems identical in structure to the initial ones. It was concluded that for novice problem solvers, general algebra rules are reflected in only a limited number of schemas. Abstraction of general rules from schemas may occur only with considerable practice and exposure to a wider range of schemas.
REPORTS ON THE EVOLUTION OF THE STUDY OF CREATIVITY. BEGINNING WITH GALTON'S STUDIES ON THE IMPACT OF HEREDITY UPON GENIUS, GUILFORD POINTS OUT THAT RELATIVELY FEW PSYCHOLOGISTS HAVE TURNED THEIR ATTENTION TO THIS PROBLEM. ONLY THOSE WHO HAVE A PARTICULAR INTEREST IN THE MEASUREMENT OF INTELLECTUAL CAPACITY HAVE BEEN UNABLE TO AVOID CONTACT WITH THE CREATIVE ASPECT OF MAN BUT THE HISTORY OF THE INTELLIGENCE TEST MOVEMENT SHOWS THAT IN ITS EARLY DEVELOPMENT IT HAS BEEN SINGULARLY DEVOID OF CONTACT WITH MEASURES OF INGENUITY, INNOVATIVE CAPACITY, OR INVENTIVENESS. SOME NONPSYCHOLOGICAL ATTEMPTS AT ATTACKING THE PROBLEM OF CREATIVITY ARE DISCUSSED. SINCE 1950 EFFORTS TO ESTABLISH THE NATURE OF CREATIVITY HAVE BEEN SOMEWHAT MORE FRUITFUL AND THE PROMISE OF MORE EFFECTIVE BASIC RESEARCH ON CREATIVE THINKING IS DISCUSSED. (32 REF.) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
When individuals think about their future, feedback on their strengths and weaknesses may often serve as a useful source of information. Three studies investigated the influence of positive and neutral moods on feedback seeking. In Studies 1 and2, positive mood increased interest in feedback about weaknesses when this information was useful for self-assessment and self-improvement. But when the feedback was not useful for these superordinate, long-term goals then positive mood directed participants’ interest to strength-focused feedback, thereby serving short-term, affective concerns (e.g., feeling good about oneself). Study 3 directly manipulated self-evaluative goals. When a learning goal was activated, positive mood increased interest in weaknesses-focused feedback, but when an affective goal was activated, positive mood increased interest in strength-focused feedback. These results support our hypothesis that positive mood attunes individuals to the relationships of goals and means, thus promoting actions that serve primary goals.
The purpose of this study was to develop a program to help cultivate divergent thinking in mathematics based on open-ended
problems and to investigate its effect. The participants were 398 seventh grade students attending middle schools in Seoul.
A method of pre- and post-testing was used to measure mainly divergent thinking skills through open-ended problems. The results
indicated that the treatment group students performed better than the comparison students overall on each component of divergent
thinking skills, which includes fluency, flexibility, and originality. The developed program can be a useful resource for
teachers to use in enhancing their students’ creative thinking skills. An open-ended approach in teaching mathematics suggested
in this paper may provide a possible arena for exploring the prospects and possibilities of improving mathematical creativity.