Educational process mining: New possibilities for understanding students' problem-solving skills

Tóth, Krisztina; Rölke, Heiko; Goldhammer, Frank; Barkow, Ingo

doi:10.1787/9789264273955-14-en

Cited by 11 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Goldhammer et al 2017a), dealing with missing responses (Weeks et al 2016), and suspected data fabrication (Yamamoto and Lennon 2018). Other studies have concentrated on the highly interactive tasks in the domain of PS-TRE (He et al 2019;von Davier 2015, 2016;Liao et al 2019;Naumann et al 2014;Stelter et al 2015;Tóth et al 2017;Vörös and Rouet 2016) and contributed to a better understanding of the adult competencies in operation.…”

Section: Review Of Research Work Using Piaac Log File Datamentioning

confidence: 99%

“…They have drawn on a large number of methods and indicators for process analysis, which include the investigation of single indicators (e.g. Tóth et al 2017) and entire action sequences (e.g. von Davier 2015, 2016).…”

Section: Studies Of the Domain Of Ps-trementioning

confidence: 99%

“…While the number of interactions and the time spent on routine steps are generic indicators applicable to several different tasks, indicators can also be highly taskspecific. Tóth et al (2017) classified the problem-solving behaviour of respondents of the German PIAAC field test using the data mining technique of decision trees. In the 'Job Search' item 1 , which was included in the PIAAC field test and now serves as a released sample task of the PS-TRE domain, respondents were asked to bookmark websites of job search portals in a search engine environment that did not have a registration or fee requirement.…”

Section: Studies Of the Domain Of Ps-trementioning

confidence: 99%

See 2 more Smart Citations

Analysing Log File Data from PIAAC

Goldhammer

Hahnel

Kroehne

2020

Methodology of Educational Measurement and Assessment

View full text Add to dashboard Cite

The OECD Programme for the International Assessment of Adult Competencies (PIAAC) was the first computer-based large-scale assessment to provide anonymised log file data from the cognitive assessment together with extensive online documentation and a data analysis support tool. The goal of the chapter is to familiarise researchers with how to access, understand, and analyse PIAAC log file data for their research purposes. After providing some conceptual background on the multiple uses of log file data and how to infer states of information processing from log file data, previous research using PIAAC log file data is reviewed. Then, the accessibility, structure, and documentation of the PIAAC log file data are described in detail, as well as how to use the PIAAC LogDataAnalyzer to extract predefined process indicators and how to create new process indicators based on the raw log data export. The Programme for the International Assessment of Adult Competencies (PIAAC) is an Organisation for Economic Cooperation and Development (OECD) study that assesses and analyses adult skills in the cognitive domains of literacy, numeracy, and problem solving in technology-rich environments (PS-TRE). The computerbased assessment requires respondents to solve a series of tasks (items). The tasks are related to information presented to the respondent on the screen (e.g. a text from a newspaper, a simulated webpage). When solving a task, the respondent interacts with the assessment system-for example, by entering or highlighting text or clicking graphical elements, buttons, or links. The assessment system logs all these interactions and stores related events (e.g. keypress) and time stamps in log files.

show abstract

Section: Review Of Research Work Using Piaac Log File Datamentioning

confidence: 99%

Section: Studies Of the Domain Of Ps-trementioning

confidence: 99%

Section: Studies Of the Domain Of Ps-trementioning

confidence: 99%

See 1 more Smart Citation

Analysing Log File Data from PIAAC

Goldhammer

Hahnel

Kroehne

2020

Methodology of Educational Measurement and Assessment

View full text Add to dashboard Cite

show abstract

“…20 In a similar context, Tóth et al 46 described how to extract process-related information from event logs, and how to use these data in problem-solving assessments and describe methods which help discover novel information based on individual problem-solving behavior.…”

Section: Computer-based Assessmentmentioning

confidence: 99%

“…Computer-based assessment Pechenizkiy et al 20 To provide feedback Tóth et al 46 To detect learning difficulties Student registration Anuwatvisit et al 48 To gain a better understanding of the underlying educational process Ayutaya et al 47 To discover learning flows Software repositories Poncin et al 50 To gain a better understanding of the software development process Poncin et al 49 Structured inquiry cycle Howard et al 51 To detect learning difficulties Jeong et al…”

mentioning

confidence: 99%

A survey on educational process mining

Bogarín

Cerezo

Romero

2017

WIREs Data Min & Knowl

159

View full text Add to dashboard Cite

Educational process mining (EPM) is an emerging field in educational data mining (EDM) aiming to make unexpressed knowledge explicit and to facilitate better understanding of the educational process. EPM uses log data gathered specifically from educational environments in order to discover, analyze, and provide a visual representation of the complete educational process. This paper introduces EPM and elaborates on some of the potential of this technology in the educational domain. It also describes some other relevant, related areas such as intentional mining, sequential pattern mining and graph mining. It highlights the components of an EPM framework and it describes the different challenges when handling event logs and other generic issues. It describes the data, tools, techniques and models used in EPM. In addition, the main work in this area is described and grouped by educational application domains.

show abstract

How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics

Molnár

2021

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

Educational process mining: New possibilities for understanding students' problem-solving skills

Cited by 11 publications

References 0 publications

Analysing Log File Data from PIAAC

Analysing Log File Data from PIAAC

A survey on educational process mining

How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics

Contact Info

Product

Resources

About