2. Complex Learning
Providing Scaffolds and Creating Contexts
Problem-based Learning: STELLAR
Creating contexts with the VMC
Complex Systems: Systems and Cycles
3. We live in a complex and dynamic world
Need to go beyond learning isolated knowledge
and facts
Useable knowledge
Soft skills (Derry & Fischer, 2007)
Preparation for lifelong learning, reasoning, and
problem solving (Fischer & Sugimoto, 2006)
Useable knowledge
Transfer, from a range of perspectives
4. Such learning:
Often situated in problem-based and inquiry
learning environments (Hmelo-Silver, Duncan, &
Chinn, 2007)
Potential for excessive cognitive load (van
Merriënboer, Kirschner, & Kester, 2003)
Appropriate scaffolding and contexts to deal
with cognitive and social challenges
5. Provide support to allow learners to
Competently do task
Learn from task
Builds on notion of ZPD (Vygotsky, 1978)
Scaffolding complex tasks through
Structuring
Problematizing
Three primary kinds of scaffolding
Communicating process
Coaching
Eliciting articulation
Hard and soft scaffolds (Saye & Brush, 2002)
6. Contextual support
Video, Hypermedia in STELLAR (Teacher Education)
VideoMosaic Collaborative Repository (VMC)
Systems and Cycles simulations
Collaboration spaces
STELLAR whiteboards, threaded discussion
Access to structured information
STELLAR Knowledge Web
VMC metadata
Systems and Cycles hypermedia
Scaffolding through Interface and activity structures
pbl online in STELLAR
VMC analytic
Systems and cycles curriculum materials
7. Processes
Collaborative knowledge building
Engagement
Learning trajectories
Outcomes
Application
Transfer
Expert perspective (e.g., Barnett & Ceci, 2002)
Preparation for future learning (e.g., Schwartz &
Bransford, 1998; Schwartz & Martin, 2004)
Actor-oriented transfer (Lobato, 2006)
9. Initial implementation (Hmelo-Silver, 2000)
Paper cases
One wandering facilitator for 6-7 groups
Limitations
Cases were oversimplification
One wandering facilitator for 6-7 groups
Difficulty identifying fruitful learning issues because
of limited and variable prior knowledge
10. Creating Context:
Provide rich video cases of practice
Concepts in context
Scaffolds
PBL online activity structure: extend skilled
facilitation resources
Knowledge Web: CFT Hypermedia support for
generating learning issues
11.
12.
13.
14.
15. Pre-post across institutions using video analysis
task
Significant pre to post gains for both sites, with
different implementations
Quasi-experimental design over 3 semesters
Participants:
101 STELLAR PBL students in 18 groups
126 comparison students from Educational Psych
subject pool Tracer concepts “Understanding”
on video analysis
Moderate to large effects over three years
Between group variability striking
16. Group 1
Facilitator
CHS
Ann
50 Fran
Cathy
Fauna
Luke
45 Other monitoring
SDL
Group monitoring
Individual monitoring
40 Grounded beliefs
Personal beliefs
Elaborations
35 Explanations
Transforming
Elaborated telling
Telling
30 Acknowledgement
Summary
Codes
Disagreements
25 Agreements
Modifications
New Ideas
Metacognitive questions
20 Explanation questions
Information questions
Personal talk
Concept talk
15
Tools as help
Tools as a problem
Task talk
10 View Other Proposals
Research Library
White Board
Discussion Board
5 Notebook
KW
Video
0
300 400 500 600 700 800 900 1000 1100
17. Group 2 Facilitator
CHS
Matt
Bob
50 Carla
Caitlin
Liz
Helen
45 Other monitoring
SDL
Group monitoring
40 Individual monitoring
Grounded beliefs
Personal beliefs
Elaborations
35 Explanations
Transforming
Elaborated telling
Telling
30 Acknowledgement
Summary
Codes
Disagreement
25 Agreement
Modifications
New Ideas
Metacognitive questions
20 Explanation questions
Information questions
Personal talk
15 Concept talk
Tools as help
Tools as a problem
Task Talk
10 View Other Proposals
Research Library
White Board
5 Discussion Board
Notebook
KW
Video
0
450 550 650 750 850 950 1050 1150 1250 1350 1450
Lines
18. Similar type of rubric developed to measure
“transfer” (Hmelo-Silver et al, 2009) on 0-3
scale
Components of transfer rubric:
1. requires understanding,
2. involves activating appropriate prior knowledge
and applying something learned in a new situation,
3. involves abstraction and cognitive flexibility,
4. can be near or far transfer, and
5. can be preparation for future learning.
19. Pretest and Posttest Scores by Class Type
Class N Pretest Posttest
STELLAR 33 0.71 (0.31) 2.02 (0.69)
Traditional 37 0.61 (0.36) 0.68 (0.34)
F (1,67) = 114.323, p < .001, d=2.55
20. Large variability in groups
Examined STELLAR whiteboards for contrasting
cases analyses
Engagement with “Transfer”
Group A
6 female students who had some difficulty
Mean gain= 1.40, SD=0.89
Group B
6 female students, rarely needed any assistance
Mean gain= 1.33, SD= 0.61
21. Discussed transfer in 3 of 4 problems
In Problem 1, Jenny proposed explanation for
enduring understanding that child in video
developed:
“In the case of Brandon, he needed to have an
understanding of how and why he was able to solve
the block problem in order to transfer his ideas onto
the pizza problem. "The first factor that influences
successful transfer is degree of mastery of the original
subject" (How People Learn, 53). Brandon was able to
continue to solve such a problem because of his
complete understanding of how he was able to arrive
at the solution for the block problem.”
22. In problem 2, Rina used the concept of transfer in
thinking about assessment as she offered this proposal:
…The portfolio should have a final summary of the students'
work and questions regarding the students' learning, so that
the students can explain and evaluate their own thinking.
(knowlege web [sic]) The students should be able to transfer
their prior knowledge of concepts such as force and motion
in order to create their vehicle, while also allowing the
activity to expand on that knowledge. …another important
facet of understanding is application (sic). Ms. Baker will
know whether the students acquired enduring
understanding by how much they can apply this knowledge
to real world problems. One way of doing is to have Ms.
Baker create another problem that will use the same
concepts in a real world setting, and evaluating whether the
students were able to apply the concepts they had learned.
23. All students mentioned transfer in P1; 2-4 students in
subsequent problems
Kathy wrote:
…Second, Brandon was able to recognize a connection
between the pizza problem and the tower problem that he
did weeks earlier. Moreover, he made this connection
relatively quickly and without much effort. He was able to
show us, by using his chart and the manipulatives (blocks),
exactly how the pizza problem mapped into the tower
problem. His understanding of the pizza problem therefore
facilitated a new, and deeper, understanding of the block
problem; this process is called transfer. Brandon’s seemingly
effortless use of transfer provides evidence that he
understood the problem, because “transfer and wide
application of learning are most likely to occur when
learners achieve an organized and coherent understanding
of the material.” (How People Learn, p. 238-239)…
24. On later problems, fluid application of concept part of shared
understanding
Micki writes:
…another way to look for enduring understanding would be the students'
transfer and application of principles of force and motion especially to
real world situations. This would show the student's understandings of
information previously and transfer it to the problem at hand, which is a
real world problem that allows students to work with hands-on material.
Mimi followed this up by incorporating Micki’s comment and a
previous proposal from another group member:
To put these two ideas together, [t]he teacher could bring together
individual explanation and transfer as evidence of enduring
understanding. An activity could be created at the end of each project
that would ask the individual members of the group to use the principles
gained to explain a real world scenario. Likewise, an activity could be
designed to facilitate transfer of the instructional objectives. For instance,
one of the objectives was learning the scientific inquiry process. The
teacher could present a real world problem that would require the
students to use the same scientific process to solve. (This would also
facilitate transfer)
25. Technology:
Scaffolds
Context
Complex measures:
Knowledge in use
More and less
productive
collaborations
26. with Carolyn Maher, Marjory Palius,
Grace Agnew, Robert Sigley, Chad
Mills
www.videomosaic.org
27. Preserves a major video data collection on student
reasoning
From diverse schools settings
To be available as open source
From 40 doctoral dissertations
Makes available new tools for
Teachers
Educators
Researchers
From longitudinal/cross sectional studies spanning
25 years
Videos following the same student cohort from
elementary school through high school and beyond
Over 4500 hours of video
29. Importance of making sense of students’
conversations and how tools mediate learning
(Hmelo-Silver, 2003)
Being aware of the contextual resources
(media, other Ss, prior experience) that Ss use
influence collaborative knowledge construction
(Arvaja et al., 2006)
Attending to social interactions in collaborative
knowledge construction (Palincsar, 1998)
30
30. Graduate Online Design
mathematics ed eCollege CMS
hybrid course Streaming video /
Four online groups linked papers
2+ week unit Minimal online
In class problem instructor intervention
solving Data
Individual study of Postings from online
videos and related threaded discussions
readings Pre and post tests
Group discussion (math, Ss reasoning)
questions
31. To what extent do videos and readings
promote online discussion within and across
groups?
How do learners relate practice to online
discussion?
To what extent do Ss relate videos to readings
in their online discussions?
32
32. All posts coded for comments related to
Video (V)
Readings (R)
Additional sub categories of comments relating
videos/readings to:
Own problem solving (PV/PR)
Others’ problem solving (OV/OR)
Earlier interventions (EV/ER)
Affect (AV/AR)
Practice (TV/TR)
33
33. Shows two groups of 10th graders (2 in one &
3 in other) working on the problem:
How many different block towers can be built,
four tall, selecting from three colors of blocks
such that the towers have at least one block of
each color?
Approximately 8 minutes
http://hdl.rutgers.edu/1782.1/rucore00000001201.Video.000062055
34
34.
35. (1) Describe Romina’s strategy for solving the Ankur’s
Challenge problem.
(2) In your opinion, is this solution a convincing one?
Why or why not?
(3) According to the Yackel & Hanna chapter, both von
Glaserfeld and Thompson equate reasoning with
learning (p. 227). From this perspective, in what ways
do explaining and justifying contribute to learning
mathematics?
36. Percent of Posted Discussions
Related to Video Activity Overall
SS Relate Videos N
a
and Readings to:
A B C D
Own problem 27 100.0% 100.0% 100.0% 83.3% 95.7%
solving (PV/PR)
Others’ problem 13 100.0% 100.0% 100.0% 85.7% 92.3%
solving (OV/OR)
Earlier 10 66.6% 100.0% 100.0% 33.3% 70.0%
interventions in the
class (EV/ER)
Enjoyment 20 100.0% 83.3% 100.0% 80.0% 90.0%
(AV/AR)
Practice (TV/TR) 24 25.0% 71.4% 75.0% 12.5% 40.7%
a
N= Number of posts
37. Percent of Posted Discussions
Relating Videos to Assigned
Topic Readings
Discussion Group
A B C D
SS relate videos to 16.7% 16.7% 45.5%* 7.1%
assigned readings
(RV)
Number of posts 18 18 11 14
38. Across all groups:
Studying videos generated reflections about own and
classmates’ problem solving
Studying videos of students’ reasoning was enjoyable
But still left us with question of ways in which
which Ss related resources to their practice
39.
40. Create multimedia artifacts using
the VMC repository
Narrative with video for purpose
Variety of uses by instructors,
researchers
Goal to classify and identify what
differentiates high quality and low
quality “analytics”
Data sources: 27 VMCanalytics from
several different classes and
researchers
Work very much in progress
Agnew et al, 2010; Hmelo-
Silver et al., in press
41. VMC Analytic by Hmelo-Silver (2011)
VMC Analytic by Horwitz (2011)
42. Math LS
Class Depth Depth Clarity Coherence
Design-
based
Research 0.78 1.00 1.22 1.33
Intro to
Math Ed 1.96 1.80 2.36 2.20
Critical
Thinking 2.00 2.40 2.30 2.00
Practicum 3.00 2.25 2.63 2.63
No Class 1.17 1.67 3.00 3.00
43. Looking deeper with contrasting case analysis
Example 1: Analytic illustrating how students can move
from particular to general
Concepts from both learning sciences and mathematics
clearly articulated
Indicative of designer's understanding of students’ learning
trajectory
Example 2: Analytic illustrates teacher questioning during
early algebra exploration
Students claims not supported by video segments selected
Textual descriptions of events were vague
Video not well chosen for intended purpose of relationship
of teacher questioning and student engagement
44. VMCAnalytics coded for
emergent themes
Explored use of word
clouds as a learning
analytic
Mathematics Education Word Cloud of coded Mathematics themes
ideas
Learning Sciences ideas
Allow us to see dominant
themes within the two Word Cloud of coded Learning Sciences themes
areas of interest
45. with Rebecca Jordan, Catherine Eberbach,
Suparna Sinha, Lei Liu, Steven Gray, Wes
Brooks, Yawen Yu
46. Goals
Learning about ecosystems
Reasoning about evidence
Modeling
6 week curriculum
Creating Contexts
Scaffolding complex
learning
Understanding how and
why along with what
48. Provide context for: Help focus on function
Discussions and behavior
Science practices Make invisible visible
Engage with complex and open for inspection
systems phenomena
49. Use the following arrows to help you decide how the evidence relates to the different
explanations:
Solid Arrow
Evidence supports the
model
Conceptual
Wavy Arrow
Evidence strongly supports
the model
Evidence contradicts
representation
X Crossed Arrow
Dashed Arrow
(disagrees with) the model
Evidence neither supports
nor contradicts the model
SBF CMP
Link each evidence box to each of the model boxes, using the arrows. On the next
page, write your reasons for the three most interesting or important arrows.
Worksheets
EMT
Teacher
Model A
Evidence 1
Model B
Evidence 2
Model C
50. Reliable pre to post test gains on systems idea
Connections across system levels
SBF
Macro-micro
Individual and group variability
51. Learning trajectories (Eberbach, Hmelo-Silver et
al., 2012)
Engagement with content (Sinha et al., 2012)
Participation in practices (Eberbach & Hmelo-
Silver, 2010; in prep)
Transfer
Multiple perspectives (Yu, Hmelo-Silver et al., 2013;
Sinha, in progress)
52. Developing ecosystems understanding is
multidimensional
AbioticBiotic
MacroMicro
StructureFunction/Behavior
Dimensions may develop differently (Wilson,
2009)
How are they influenced by particular aspects of
instruction?
Classroom microgenetic analysis (Chinn, 2006)
53.
54. 3
B:A
M:M
2
Extraneous
SBF
Coherence
1
Pre AA1 AA2 Post
55. Often a goal of schooling– but hard to find in
the lab
Do students transfer ecosystems concepts from
one context to another?
Aquatic Rainforest
Focus on tracer concepts that were targets of
instruction
Photosynthesis
Cellular Respiration
Decomposition
56.
57. Example of presence
of “plants use
energy” and
Example of “organic compounds
presence of are produced”
“gas is
exchanged”
58. AOT perspective focuses on how students
perceive similarities (Lobato, 2006; Sinha et
al., 2010)
Pre and post interviews of 38 students from 2
schools
Task
Label groups EMT model in terms of CMP
Label EMT model of
59.
60. Frequencies of students’ generalization of CMP
Levels of CMP transfer
Total n No C C&P C&M CMP
Transfer transfer transfer transfer transfer
38 3 (8%) 15 (39%) 7 (18%) 1 (3%) 12 (31%)
61. Work in progress (Sinha et al, in prep)
Goal: Relating engagement to transfer
Social coordination
Behavioral
Task
ConceptualConsequential
Coding of video as students work on modeling
and simulations
5 min intervals
62. Coding Conceptual-Consequential (CC)
Engagement
High (3) Medium (2) Low (1)
Connects to other Focused on content Focus on low-level
sources of knowledge connections and declarative knowledge;
and experiences conceptual facts
understanding, but do
Reflects on larger not necessarily reflect or
question or problem (e.g. go back the central
why do fish die). question or relate to the
real world.
‘0 ‘ Code was assigned when teacher was addressing the entire class.
63. • Creating need to know
Context
Scaffolds • Technology allows engagement
Learner
with complex phenomena
activity
• Video
• Simulations
• Distributed scaffolding help
Complex understanding manage complexity
• BUT need to examine both
participation and outcomes
Will now present some examples in the context of my work– will talk about technology
----- Meeting Notes (9/5/12 17:17) -----STELLAR was an attempt to support problem-based learning in teacher education through the uses of technology.
- analyze only steps 3 to 6 of the activity for each group, the line counts begin at different numbers for each group because Group 2 engaged more with the STELLAR tools in the earlier phases of the activity than did Group 1.The vertical axis shows the categories of tool hits, discourse codes, and speakers. The horizontal axis shows the number of tool-related events, either a log entry or a discourse turn. The bottom seven categories represent tool hits by any member of the group. The top six or seven categories represent the speakers.Group 1- weaker group, in Group 1, the facilitators were involved early and fairly frequently, and asked most of the explanatory and metacognitive questions. Ann and Fauna seemed to dominate the discourse though other students contributed. Task talk continued throughout duration of activity as they struggled to figure out what they were doingAnother aspect of how the CORDTRA diagrams help us distinguish the collaborative activity between two groups is by showing the overall relation between the discourse and the tool use. In Group 1, the students initially viewed the video and the Knowledge Web but after about line 650, none of the group members used these two resources until the very end of the collaborative phase at line 1000. The content of their online postings were intermixed conceptual, social, task and tool-related talk throughout their work on the problem. There was some discussion of tools as a problem midway through the discussion. ----- Meeting Notes (9/5/12 18:03) -----To better understand the variability, we created visual representation in which we examined the relation between discourse features and log data from using the various tools.CORDTRA-- builds on work of Rose Luckin-- Chronologically ordered representation of discourse and tool related activity all on a single timeline.
Group 2In Group 2, the facilitators joined in much later and made infrequent contributions in the form of questions. This is because in this group the students themselves asked a number of questions throughout the duration of the problem. The group participated fairly evenly except for Matt who chimed in late in the group’s work. His contribution built on one of the other student’s ideas and was grounded in personal experience. His later contributions offered an important new idea for an activity that was grounded in psychological theory. What is interesting in Group 2 is that they went back to the video and the Knowledge Web at intervals throughout the discussion (e.g., around lines 850-925, 1050-1100, 1275). It appears that group members did this following several explanation questions as Figure 7 demonstrates (for example at about line 1050). This suggests that Group 2 was using the resources of the video and the Knowledge Web in a purposeful manner. That is, they seemed to be using the STELLAR resources to answer the questions at hand. They were bringing together the problems of practice and conceptual ideas repeatedly. We hypothesize that it is the repeated meshing of conceptual ideas from the Knowledge Web with the perceptual ideas from the video, which is a precondition for transfer (see Derry, 2006). Our current analysis, however, did not test to see whether transfer in fact occurs.The CORDTRA demonstrates several other distinctions between the two groups with regards to timing. Group 2 engaged in group monitoring throughout the activity whereas Group 1 did not engage in this kind of monitoring until relatively late in the activity. This was generally a request for feedback from the rest of the group. Inspection of the data shows that it was the facilitator doing most of the group monitoring in Group 1 whereas in Group 2, it was the students who were the ones monitoring themselves. The facilitators adapted their support to the needs of the group and thus differentially engaged with each group as needed. ----- Meeting Notes (9/5/12 18:03) -----There are other distinctions I could mention, but from a methodological perspective, this allowed us to see how the different groups engaged in their problem solving task.
Participants viewed a brief video in which high school students learned about electricity, electrical circuits, and how a light bulb works. Before viewing the video, they received a brief written explanation describing how the video clip illustrated a problem the teacher had: that even top students were maintaining their pre-course misconceptions about electricity after instruction. The video explained how the teacher had spent a month covering advanced topics in electricity and provided hands-on experience designed to reinforce those concepts and illustrate how electricity enabled a light bulb to work. Video also showed an interview with a good student before and after instruction and demonstrated that she maintained the same misconceptions following instruction.They had thirty minutes to answer the following four questions: (1) How do you know that the student failed to learn? (2) Why did the student fail to learn? (3) What recommendations would you make to the teacher to help him improve his teaching? and (4) What else do you need to know to better understand the teaching-learning situation? What additional questions would you ask?
Group A- difficultydemonstrated by the need for the teaching assistant to frequently intervene and facilitate this group’s workGroup B- Both groups engaged with the concept of transfer and they showed this in how they used the technology
All students discussed in P1, number decreased in subsequent problems. All the students in this group used transfer in their independent proposals but did not use it as an idea in commenting on other proposals, suggesting that the members of this group used the concept in parallel without real knowledge building Jenny quote 1: This excerpt shows that the student brought in ideas about transfer through direct quotes that define the concept. This is much like knowledge telling (Bereiter & Scardamalia, 1987), as Jenny shared relatively unprocessed information with her group. Although this group continued to use the concept in independent proposals, it later became encapsulated as part of the group’s language and no longer required detailed explanation.
illustrates a more fluid use of the concept of transfer without needing to provide all the definitions, suggesting that the group members had achieved a shared understanding. This use of conceptual ideas was seen to an even greater degree in Group B.
, similar to Group A, the group members began by incorporating direct quotes as they shared information, as this excerpt from Kathy shows. But unlike the other group– she more carefully mapped the definition to the evidence in the video.
Preserves video and metadata by converting to digital formatOffers a research and teaching portalProvides analytic tools, customized ontologies, and personal spaceSupports individual work and group collaboration
Different methods help deal with the complexity of theoretical perspectives
Same pre and post tests as in other enactments– but here our research questions focused on how people used the VMC resources
Posted Assignment This week's assignment for online work involves a video and two readings, with threaded discussion, that follows class work on problem solving for the Ankur's Challenge task. The following questions are intended to guide discussion in your small groups (and will also be posted in the introduction to group discussion threads).
ACROSS GROUPS, THE PERCENT OF POSTED DISCUSSIONS RELATING VIDEOS TO OWN AND OTHERS’ PROBLEM SOLVING WAS STATISTICALLY SIGNIFICANT.THE PERCENT OF POSTED DISCUSSIONS ABOUT ENJOYMENT ACROSS GROUPS WAS STATISTICALLY SIGNIFICANT.GROUPS A & D MADE LESS REFERENCE TO EARLY INTERVENTIONS AND TO PRACTICE .
TABLE 2 SHOWS THAT FOR GROUP C, THE PERCENTAGE OF SS WHO RELATED VIDEOS TO ASSIGNED READINGS WAS STATISTICALLY SIGNIFICANT,
Answered this with qualitative analysis
Purposes include:purpose (e.g., research, professional development)We also wanted, for classes, to better understand design features
Part of goal was to get sense of terrain.
Used this to look at high and lesser quality analytics to help future instructors better design tasks and evaluate analytics. Help us in discerning additional criteria that distinguished high and low quality
This was derived from researcher-supplied themes– will be interesting to apply to the raw data.
Talk a little bit about move
Embedded in problem- design aquarium ,video of eutrophication of pond,
Describe the screen – variables, controls, graph, outputs, fish… controls: sunlight, nutrient runoffDescribe the screen and the purpose of this simulation. At this point they have already identified inverse relationships between O2 and CO2. That when the O2 is down, more fish die. The teacher has just reminded the whole class that the goal of the activity is to model how the fish died in the pond—to reproduce the conditions in the pond that resulted in the fish dying. They have been modeling a system that keeps the fish alive or to die of old age. Why is the O2 decreasing?----- [NEXT SLIDE]
Move from SBF CMP– need to explain this.
Note that a great deal of this work is in progress.
Typically, we look at an aggregate pre to post test gain, but in complex domains, this is not sufficient- need to know how students learn– where they start, which aspects of systems are harder than others.Question 1 we are beginning to gain traction on– analysis is ongoing– question 2 is on the burner/Microgenetic analysis
Coded from Drawings taken over 4 time points in a year--
To support our analysis, we constructed a graph) that depicts the group means for each systems dimension in Classroom 1. Students appear to have steep growth towards higher levels of Biotic/Abiotic and macro/micro dimensions but relatively more steady growth towards higher levels of SBF and Coherence dimensions that converge by posttest. From the start, students represented structures in terms of their behaviors or functions and made connections between phenomena (Coherence). However, these structures were primarily macroscopic and biotic. Students made more complex connections between Biotic/Abiotic structures (AA1) and between Macro/Micro structures (AA2) before reaching higher levels of SBF or Coherence. Finally, the inclusion of extraneous structures peaked at AA1 and then declined until no drawings included extraneous structures by posttest.Of course, means mask some of the variability but we are seeing some consistency in B:A preceding M;M in general and that making both of these are important for a coherent understanding– represented by both the coherence measure and sBF score.
We designed the interview to assess students’ generalization of mechanistic reasoning and to make sense of new problems related to aquatic ecosystems. In the first problem, students were shown a paper copy of their group-generated EMT model depicting factors that may have led to fish dying suddenly in a local pond. The students were then asked to label the model in terms of CMP and explain their reasoning. In the model (see Figure 1), the entire problem reflected the phenomena, the rectangular boxes represented components that were linked together by explanations of their mechanistic behavior. In the second problem, students were told that there has been a sudden increase in geese population around a lake that has resulted in changes to the aquatic ecosystem. They were shown three versions of EMT models (the first consisting only of components, the second had only mechanisms but no components, and the third consisted of numerous components, mechanisms connecting them and phenomena). Students were first asked to rank each model on a scale of one to three, with three being the most complete explanation about what happened to the lake ecosystem as a result of the overpopulation of geese. Next, students labeled the model they had ranked the highest and were asked to explain the criteria for making their selection.
To trace generalization of mechanistic reasoning from an AOT lens, we compared each student’s labeled model in task 1 with that in task 2. We identified items they labeled as identical in terms of CMP in both tasks and also kept track of areas where they exhibited differences (i.e., identified phenomena in one task as the entire model and in the other had it labeled as a mechanism).
As we compared students’ labeling of models, it was evident that a majority of students generalized the concept of components and phenomena. We observed that students would either circle the entire model or focus on the primary problem under investigation when asked to label phenomena. Additionally, almost all the students were quick to tag components as factors that may have led to the problem
Social coordinationBehavioral engagement refers to the degree of the group’s on-task behavior.Task engagement refers to the focus of engagement on efficient planning and what steps to take next to accomplish the task. Can also refer to a focus on manipulating the technology tool (but the rating does not reflect whether concepts or content represented).
Conceptual engagement is considered a continuum that ranges from content connections that are focused on the key question or task problem or relating to the real world/experiences to simple knowledge telling. Note: The conceptual and consequential connections do not need to be accurate.To warrant a high rating, students need to go beyond referencing the larger question of why do fish die, for example, to making a connection with evidence or content. Also, a connection to a simulation or piece of evidence is still a medium rating, unless connected to the larger question.Medium: Group discusses and identifies content relationships between visible and invisible components.Low: simply reading the hypermedia; only interpreting simulation data without content relation (nutrients go up).We are seeing trends that the simulations foster high conceptual consquential engagement, task engagement, and behavioral engagement– more variability with EMT modeling tool.
Technology can play important role, but is synergistic with creating engaging contexts that create need for knowledge, bringing hard and soft scaffolds together– thinking about how to distribute scaffolding– also need to examine learner activity
