Algoritmilise mõtlemise oskuste hindamise mudel

Väitekirja elektrooniline versioon ei sisalda publikatsiooneTehnoloogia on kõikjal meie ümber ja arvutiteadus pole enam ainult eraldi distsipliin teadlastele, vaid omab aina laiemat rolli ka teistel aladel. Huvi algoritmilise mõtlemise arendamise vastu kasvab kõigil haridustasemetel alates eelkoolist lõpetades ülikooliga. Sellega seoses vajame aina enam üldhariduskoolide tasemel uuringuid, et omada paremat ülevaadet algoritmilise mõtlemise oskustest, et luua praktiline mudel algoritmilise mõtlemise hindamiseks. Algoritmilist mõtlemist kirjeldatakse paljudes artiklites, kuid sageli pole need omavahel kooskõlas ja puudub ühine arusaamine algoritmilise mõtlemise oskuste dimensioonidest. Doktoritöö sisaldab süstemaatilist kirjanduse analüüsi, kus mõjukamate artiklite sünteesimisel jõutakse kolmeetapilise algoritmilise mõtlemise oskuste mudelini. See mudel koosneb järgnevatest etappidest: i) probleemi defineerimine, ii) probleemi lahendamine ja iii) lahenduse analüüsimine. Need kolm etappi sisaldavad kümmet algoritmilise mõtlemise alamoskust: probleemi formuleerimine, abstrahheerimine, reformuleerimine, osadeks võtmine, andmete kogumine ja analüüs, algoritmiline disain, paralleliseerimine ja itereerimine, automatiseerimine, üldistamine ning tulemuse hindamine. Selleks, et algoritmilist mõtlemist süstemaatiliselt arendada, on vaja mõõtevahendit vastavate oskuste mõõtmiseks põhikoolis. Doktoritöö uurib informaatikaviktoriini Kobrase ülesannete abil, milliseid algoritmilise mõtlemise osaoskusi on võimalik eraldada Kobrase viktoriini tulemustest lähtuvalt ilmnes kaks algoritmilise mõtlemise oskust: algoritmiline disain ja mustrite äratundmine. Lisaks põhikoolile kasutati ülesandeid ka gümnaasiumis millga kinnitati, et kohendatud kujul saab neid ülesandeid kasutada algoritmilise mõtlemise oskuste hindamiseks ka gümnaasiumisgümnaasiumitasemel. Viimase asjana pakutakse doktoritöös välja teoreetilisi ja empiirilisi tulemusi kokkuvõttev algoritmilise mõtlemise oskusi hindav mudel.In the modernizing world, computer science is not only a separate discipline for scientists but has an essential role in many fields. There is an increasing interest in developing computational thinking (CT) skills at various education levels – from kindergarten to university. Therefore, at the comprehensive school level, research is needed to have an understanding of the dimensions of CT skills and to develop a model for assessing CT skills. CT is described in several articles, but these are not in line with each other, and there is missing a common understanding of the dimensions of the skills that should be in the focus while developing and assessing CT skills. In this doctoral study, through a systematic literature review, an overview of the dimensions of CT presented in scientific papers is given. A model for assessing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills: problem formulation, abstraction, problem reformulation, decomposition, data collection and analysis, algorithmic design, parallelization and iteration, automation, generalization, and evaluation. The systematic development of CT skills needs an instrument for assessing CT skills at the basic school level. This doctoral study describes CT skills that can be distinguished from the Bebras (Kobras) international challenge results. Results show that wto CT skills emerged that can be characterized as algorithmic thinking and pattern recognition. These Bebras tasks were also modified to be used for setting directions for developing CT skills at the secondary school level. Eventually, a modified model for assessing CT skills is presented, combining the theoretical and empirical results from the three main studies.https://www.ester.ee/record=b543136

Computation across the curriculum: What skills are needed?

Computation, the use of a computer to solve, simulate, or visualize a physical problem, has revolutionized how physics research is done. Computation is used widely to model systems, to simulate experiments, and to analyze data. Yet, in most undergraduate programs, students have little formal opportunity to engage with computation and, thus, are left to their own to develop their computational expertise. As part of a larger project to study how computation is incorporated in some undergraduate physics programs (and how it might be incorporated further), we convened a mini-conference and conducted a series of interviews with industry professionals, academic faculty, and employed bachelor's graduates who make use of computation in their everyday work. We present preliminary results that speak to how participants developed the requisite skills to do professional computational work and what skills they perceive are necessary to conduct such work.Comment: 4 pages; accepted to 2015 Physics Education Research Conference Proceeding

Developing computational thinking in the classroom: a framework

Computational thinking sits at the heart of the new statutory programme of study for Computing: “A high quality computing education equips pupils to use computational thinking and creativity to understand and change the world” (Department for Education, 2013, p. 188). This document aims to support teachers to teach computational thinking. It describes a framework that helps explain what computational thinking is, describes pedagogic approaches for teaching it and gives ways to assess it. Pupil progression with the previous ICT curriculum was often demonstrated through ‘how’ (for example, a software usage skill) or ‘what’ the pupil produced (for example, a poster). This was partly due to the needs of the business world for office skills. Such use of precious curriculum time however has several weaknesses. Firstly, the country’s economy depends on technological innovation not just on use of technology. Secondly, the pace of technology and organisational change is fast in that the ICT skills learnt are out of date before a pupil leaves school. Thirdly, technology invades all aspects of our life and the typically taught office practice is only a small part of technology use today

The abstraction transition taxonomy: developing desired learning outcomes through the lens of situated cognition

We report on a post-hoc analysis of introductory programming lecture materials. The purpose of this analysis is to identify what knowledge and skills we are asking students to acquire, as situated in the activity, tools, and culture of what programmers do and how they think. The specific materials analyzed are the 133 Peer Instruction questions used in lecture to support cognitive apprenticeship -- honoring the situated nature of knowledge. We propose an Abstraction Transition Taxonomy for classifying the kinds of knowing and practices we engage students in as we seek to apprentice them into the programming world. We find students are asked to answer questions expressed using three levels of abstraction: English, CS Speak, and Code. Moreover, many questions involve asking students to transition between levels of abstraction within the context of a computational problem. Finally, by applying our taxonomy in classifying a range of introductory programming exams, we find that summative assessments (including our own) tend to emphasize a small range of the skills fostered in students during the formative/apprenticeship phase

Hiding in Plain Sight: Identifying Computational Thinking in the Ontario Elementary School Curriculum

Given a growing digital economy with complex problems, demands are being made for education to address computational thinking (CT) – an approach to problem solving that draws on the tenets of computer science. We conducted a comprehensive content analysis of the Ontario elementary school curriculum documents for 44 CT-related terms to examine the extent to which CT may already be considered within the curriculum. The quantitative analysis strategy provided frequencies of terms, and a qualitative analysis provided information about how and where terms were being used. As predicted, results showed that while CT terms appeared mostly in Mathematics, and concepts and perspectives were more frequently cited than practices, related terms appeared across almost all disciplines and grades. Findings suggest that CT is already a relevant consideration for educators in terms of concepts and perspectives; however, CT practices should be more widely incorporated to promote 21st century skills across disciplines. Future research would benefit from continued examination of the implementation and assessment of CT and its related concepts, practices, and perspectives

Assessing collaborative learning: big data, analytics and university futures

Traditionally, assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions, of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally-stored student activity data, open new practical and epistemic possibilities for assessment and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address 21st Century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts

ScratchMaths: evaluation report and executive summary

Since 2014, computing has been part of the primary curriculum. ‘Scratch’ is frequently used by schools, and the EEF funded this trial to test whether the platform could be used to improve pupils’ computational thinking skills, and whether this in turn could have a positive impact on Key Stage 2 maths attainment. Good computational thinking skills mean pupils can use problem solving methods that involve expressing problems and their solutions in ways that a computer could execute – for example, recognising patterns. Previous research has shown that pupils with better computational thinking skills do better in maths. The study found a positive impact on computational thinking skills at the end of Year 5 – particularly for pupils who have ever been eligible for free school meals. However, there was no evidence of an impact on Key Stage 2 maths attainment when pupils were tested at the end of Year 6. Many of the schools in the trial did not fully implement ScratchMaths, particularly in Year 6, where teachers expressed concerns about the pressure of Key Stage 2 SATs. But there was no evidence that schools which did implement the programme had better maths results. Schools may be interested in ScratchMaths as an affordable way to cover aspects of the primary computing curriculum in maths lessons without any adverse effect on core maths outcomes. This trial, however, did not provide evidence that ScratchMaths is an effective way to improve maths outcomes

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

What counts as numeracy?

The purpose of the study was to infer the Scottish HMI view of what is meant by Numeracy given the concerns that primary children's achievements in Numeracy reflect a lack of flexibility in handling number and an overemphasis on procedures at the expense of understanding (HMI, 1997). Three hundred HMI reports on primary schools in Scotland were randomly selected. Content analysis of the sections on Number, Money and Measurement revealed Numeracy to be conceived of as computational proficiency and as understanding of number. Surprisingly, there were significantly more (p<0.05) references to computational proficiency than there were to understanding of number. The results are discussed in terms of what it means to understand number. It is suggested that there needs to be much clearer delineation of what is required and meant by the idea of understanding number