SEQUENTIAL AND TEMPORAL DATA Jonna Malmberg

WHAT CAN SEQUENTIAL AND TEMPORAL DATA REVEAL?

WHAT IS SEQUENTIAL AND TEMPORAL DATA? Data about the (learning) process Quite complete (relative to what it is possible to observe) Very fine-grained, time-stamped representation Video data, eye movement data, log file traces, chat data – Unobtrusive – data are created as learners do what they do Perry & Winne, 2011

WHY TEMPORAL AND SEQUENTIAL DATA The way in which students engage in self-regulated learning (and SSRL) is affected by previous learning experiences and contextual, situation-specific features (Azevedo et al, 2010). - The events are not independent (Hesse, 2013) Analysing temporal sequences of SRL and SSRL informs regulatory processes as they unfold (Järvelä & Fisher, 2014) How to identify temporal sequences of SRL and SSRL?

DATA EXAMPLE Case study from higher education context 18 graduate students worked in 6 groups over an 8-week in a “Learning for Understanding” course The course involved three learning phases, each focusing on a specific topics. The collaborative tasks were constructed to be challenging and require multiple student perspectives.

nSTUDY LEARNING ENVIRONMENT (Winne, Hadwin & Beaudoin, 2010) Support for self and shared regulation of learning Planning – and reflection notes Chat discussions Data sources: Recorded log file traces chat discussions

LOG FILE TRACES Example of Logfile traces from nStudy learning environment (Winne et al., 2011) OpenWindowAction E :26:01 GotFocusAction E :26:07 BrowserDocumentOpened E :26:08 LostFocusAction E :26:09 BrowserDocumentOpened E :27:43 GotFocusAction E :27:46 LostFocusAction E :27:46 OpenWindowAction E :27:46 ClickButtonAction E :27:46 GotFocusAction E :27:50 FolderSelected E :28:29 FolderSelected E :28:32 FolderSelected E :28:33 FolderSelected E :28:36 ItemSelected E :28:38 NoteWindow.Save E :28:38 LostFocusAction E :28:38 OpenWindowAction E :28:39 GotFocusAction E :28:40 BrowserDocumentOpened E :28:40 LostFocusAction E :29:20 BrowserDocumentOpened E :29:22 BrowserDocumentOpened E :29:26 Usually thousands of rows of information Not all the information is important View events = When something is opened Model events = When something is a)Saved b)Updated c)Deleted

How these events are sequenced How to define a sequence? What events exactly? HOW CAN WE FIND THEM? How to DEFINE interesting activities?

SEQUENTIAL AND TEMPORAL ANALYSIS (e.g. Johnsson, D’Mello & Azevedo, 2012; Clearly, Callan & Zimmerman, 2012; Molenaar & Järvelä, 2013; Malmberg, Järvenoja & Järvelä, 2013) on – line chat discussions log file traces Learning task outcomes SSRL (f=35) SRL (f=321) 1. BEFORE 2. DURING 3. AFTER SRL SSRL SRL MICROLEVEL EXAMPLES UNIT OF ANALYSIS: TIMING OF SRL + SSRL EVENTS

DATA ANALYSIS – LOG FILE TRACES… (nStudy, Winne et al., 2010) Trace data activityTheoretical definition Frequency Internal actions248 View Task Instruction (TI)Task understanding101 View Planning Note (VP)Task understanding60 View Edited Planning Note (VEP)Monitoring56 View Edited Reflection Note (VER)Monitoring31 Interactive actions73 Edit Reflection Note (ER)Evaluating45 Edit Planning Note (EP)Planning28

DATA ANALYSIS – ON-LINE CHAT DISCUSSIONS Coded SSRL episodes (6188 lines) (meaningful collective interaction chat episodes; Greeno, 2006) Socially shared task understanding3 Socially shared planning17 Socially shared strategy use4 Socially shared motivation11 Total SSRL codings35

LEARNING TASK OUTCOMES TASKOUTCOMEM Collaborative learning outcomes from each three learning task among the five groups were coded and categorized on a likert scale varying from 1 to 4 (Biggs 1984). 1= lowest, 4= highest score

MICROLEVEL DATA EXAMPLE Integration of coded chat and log data INTERNALINTERACTIVEMICROLEVEL SEQUENCE OF SHARED REGULATION Task UnderstandingSocially shared strategyTask understandingSocially shared strategy VPTI SSTRTUSSTR ….This is how it looks in analytical level… Self-regulated learning: TI=Task Instructions VP= View Planning Socially Shared Regulation: SSTR= Socially shared strategy +=

RESULTS 3 What characterizes temporal sequences of self- and shared regulation activities in high – and low learning outcome situations? Example 1 of self- and shared regulation in HIGH learning outcome task Example 2 of self- and shared regulation in LOW learning outcome task TU=Task understanding: MON= Monitoring; PL= Planning; REF= Reflection; SSPL= Socially shared planning; SSM=Socially shared motivation regulation

TO CONCLUDE… Analysing temporal sequences of SRL and SSRL informs regulatory processes as they unfold Simplified patterns inform about changes in regulation in contrasting cases Important details can be lost? Generalisation of findings to different settings?

OTHER ANALYTICAL APPROACHES State lag sequential analysis (e.g. Bakeman & Quera, 2007) Process discovery (Gunther & VandeAalst, 2007) – Fuzzy mining algorithm Data mining & parsing (e.g. computer generated logfile traces) (Romero & Ventura, 2007)