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.

Cognitive, Motivational and Emotional Base for Learning for Understanding Task 1 Task 2 Task 3

Learning sciences and Self-regulated learning

Memory structures and Learning strategies

Motivation regulation and Instructional principles

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 1.32205E+12 2011-11-23 05:26:01GotFocusAction 1.32205E+12 2011-11-23 05:26:07BrowserDocumentOpened 1.32205E+12 2011-11-23 05:26:08LostFocusAction 1.32205E+12 2011-11-23 05:26:09BrowserDocumentOpened 1.32205E+12 2011-11-23 05:27:43GotFocusAction 1.32205E+12 2011-11-23 05:27:46LostFocusAction 1.32205E+12 2011-11-23 05:27:46OpenWindowAction 1.32205E+12 2011-11-23 05:27:46ClickButtonAction 1.32205E+12 2011-11-23 05:27:46GotFocusAction 1.32205E+12 2011-11-23 05:27:50FolderSelected 1.32205E+12 2011-11-23 05:28:29FolderSelected 1.32205E+12 2011-11-23 05:28:32FolderSelected 1.32205E+12 2011-11-23 05:28:33FolderSelected 1.32205E+12 2011-11-23 05:28:36ItemSelected 1.32205E+12 2011-11-23 05:28:38NoteWindow.Save1.32205E+12 2011-11-23 05:28:38LostFocusAction 1.32205E+12 2011-11-23 05:28:38OpenWindowAction 1.32205E+12 2011-11-23 05:28:39GotFocusAction 1.32205E+12 2011-11-23 05:28:40BrowserDocumentOpened 1.32205E+12 2011-11-23 05:28:40LostFocusAction 1.32205E+12 2011-11-23 05:29:20BrowserDocumentOpened 1.32205E+12 2011-11-23 05:29:22BrowserDocumentOpened 1.32205E+12 2011-11-23 05:29:26

• Usually thousands of rows of information

• Not all the information is important

View events = When something is opened Model events = When something is a) Savedb) Updatedc) 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

1234

SSRL (f=35)SRL (f=321)

1. BEFORE2. DURING3. AFTER

SRL SRLSSR

L

SRL SRL SRL SRL

MICROLEVEL EXAMPLES

1

2

3

4

5

UNIT OF ANALYSIS: TIMING OF SRL + SSRL

EVENTS

DATA ANALYSIS – LOG FILE TRACES… (nStudy, Winne et al., 2010)

Trace data activity Theoretical definition

Frequency

Internal actions 248

View Task Instruction (TI) Task understanding 101

View Planning Note (VP) Task understanding 60

View Edited Planning Note (VEP)

Monitoring 56

View Edited Reflection Note (VER)

Monitoring 31

Interactive actions 73

Edit Reflection Note (ER) Evaluating 45

Edit Planning Note (EP) Planning 28

DATA ANALYSIS – ON-LINE CHAT DISCUSSIONS

Coded SSRL episodes (6188 lines)(meaningful collective interaction chat episodes; Greeno, 2006)

Socially shared task understanding 3

Socially shared planning 17

Socially shared strategy use 4

Socially shared motivation 11

Total SSRL codings 35

LEARNING TASK OUTCOMES

TASK OUTCOME M

1 4 3 2 1 1 2.2

2 2 1 1 3 4 2.2

3 2 3 1 3 4 2.6

• 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 EXAMPLEIntegration of coded chat and log data

INTERNAL INTERACTIVE MICROLEVEL SEQUENCE OF SHARED REGULATION

Task Understanding Socially shared strategy

Task understanding

Socially shared

strategy

 

VP TI TI TI SSTR TU SSTR  

….This is how it looks in analytical level…

Self-regulated learning:TI=Task InstructionsVP= View Planning

Socially Shared Regulation:SSTR= Socially shared strategy

+ =

RESULTS 3What 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

BEFORE DURING AFTER

1 TU SSTU SSPL 1 TU PL 1 SSM REF 2 MON TU PL 2 MON REF SSPL 3 TU SSM 3 MON REF 4 MON

BEFORE DURING AFTER

1 TU PL 1 MON TU MON 1 REF 2 MON 2 TU MON

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)