The Learning Tree: A Definition and Exploration

The "learning tree" is a potent symbol representing growth, knowledge, and the interconnectedness of life experiences. It is frequently used in visual arts and film to embody the idea that learning is a lifelong journey shaped by personal and collective histories, as well as community connections. This motif underscores themes of resilience, growth, and the importance of nurturing one's roots.

Origins and Meaning

The Learning Tree was originally a novel by Gordon Parks, which later became a film that explores themes of racism, identity, and personal growth. The tree serves as a metaphor for the interconnectedness of individuals within a community, highlighting how experiences shape one's identity and perspective. This narrative foundation provides a rich backdrop for understanding the symbol's deeper meanings.

Visual Representation and Symbolism

Visual representations of the learning tree often include diverse elements such as roots, branches, and leaves, symbolizing the depth of knowledge and various pathways of learning.

Roots: Represent the foundational knowledge, cultural heritage, and personal history that ground an individual.
Branches: Symbolize the various paths and experiences one encounters throughout life, each contributing to the growth of understanding.
Leaves: Represent individual pieces of knowledge, skills, and insights gained through learning.

Application in Film

In film, the learning tree motif can be seen in the way characters evolve through their experiences and relationships, reflecting broader societal issues. Filmmakers use this symbol to visually represent characters' journeys, showing how their experiences are interwoven with their cultural heritage. This adds layers to the narrative, allowing viewers to connect emotionally with the characters' struggles and triumphs.

Educational Programs and Resources

Various educational programs utilize the concept of trees and forests to promote learning and environmental stewardship. These programs often incorporate hands-on activities and interdisciplinary approaches to engage learners of all ages.

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Project Learning Tree (PLT)

Project Learning Tree (PLT) offers a variety of curricula designed to support educators in teaching about the environment. These programs cater to different age groups and learning objectives, emphasizing hands-on activities and critical thinking skills.

Early Childhood Education

PLT’s Early Childhood guide encourages children to use their senses to explore, discover, and communicate in expressive ways. It emphasizes outdoor adventure, supports children having fun while learning, and uses each child’s imagination and creativity to provide opportunities for learning in groups or as individuals. The activities include opportunities to incorporate music and movement using the CD to encourage children to sing and dance. This program specifically meets the needs of early childhood educators and younger learners and is designed to teach young children about their environment and how they can make a difference, while developing their skills in language, mathematics, science, and more. PLT’s GreenSchools for Early Childhood includes an Educator Guide and five Investigations. To receive the materials, register to get access to each investigation and the educator guide online.

K-12 Education

PLT provides a range of guides and activities suitable for K-12 students, focusing on various environmental topics such as trees and forests, wildlife, water, air, energy, waste, climate change, invasive species, and community planning. This guide contains 96 interdisciplinary, hands-on activities that bring the environment into the classroom and students into the environment. These fun activities have an emphasis on science, reading, writing, mathematics, and social studies to engage students in learning - both outside and indoors. Each activity is tailored to specific grade levels and learning objectives and filled with opportunities to build critical thinking skills and differentiated instruction techniques. The following secondary modules require a minimum of a two-hour workshop for each module.

Community Engagement

PLT also offers modules that encourage students to explore their communities and address environmental issues. All communities - urban, suburban, small town, rural - are experiencing growth and change. Students discover their own backyards and work as community members to protect the environmental, social, and economic integrity of the places we live. Students explore the roots and solutions of this universal environmental issue. Students learn how to assess environment and health issues, and about the tools they can use to make good decisions.

GreenSchools Program

Five hands-on, student-driven investigations are at the heart of the PLT GreenSchools program. These units may be purchased or obtained by taking the corresponding online course through www.plt.org. The investigations include:

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Treemendous Science!: Designed to help K-2 students explore and collect tree data to develop understandings about how trees grow, the roles trees play in ecosystems, and the ways in which trees and humans interact. A unique feature of Treemendous Science! is that it is organized around three levels, which approximately correspond to kindergarten (Level A), first grade (Level B), and second grade (Level C).
Energy in Ecosystems: Designed for students in grades 3-5, Energy in Ecosystems investigates the ways in which organisms depend on each other to survive and thrive. Students focus on forests-one of the largest and most complex types of ecosystems-and come to understand some of the interactions present in all ecosystems.
Climate Change: Perhaps more than any other environmental issue, the topic of climate change challenges science teachers to accurately convey data, reveal assumptions, and engage critical-thinking skills.
Discover Your Urban Forest: Discover Your Urban Forest is a downloadable resource for educators of students in grades 6-8 that invites learners to explore their urban environment and investigate environmental issues that affect their urban community. Three hands-on activities, with an emphasis on science and social studies, engage students in learning about the place they live and how we depend on natural systems to sustain us. Students learn to value diverse perspectives about different landscapes whether it is a city sidewalk, an urban forest, or a community park. The activities can be used as individual, stand-alone lessons, or all together as a cohesive unit of instruction using a storyline technique.
Sensational Trees: Sensational Trees is a downloadable resource for educators of students grades K-2 that invites young learners to investigate trees using their senses.
Biodiversity Blitz Trees: Biodiversity Blitz Trees is a downloadable, password-protected PDF for educators of students in grades 6-8 that invites young learners to investigate the variety of species in an ecosystem, and how this variety - or biodiversity - helps sustain life on Earth. Three hands-on activities, with an emphasis on science, English language arts, math and social studies, engage students in learning about why biodiversity is one of the most important indicators of an ecosystem’s health, and how greater biodiversity means a greater ability to cope with change.
Green Jobs: Exploring Forest Careers: Help youth discover careers in sustainable forestry and conservation. Green Jobs: Exploring Forest Careers includes four hands-on instructional activities to help youth research forestry jobs, and practice managing and monitoring forest resources. It is designed for educators, career and guidance counselors, Scouts, 4-H, and FFA leaders, foresters, and job training advisors to use with learners aged 12-25. Today’s youth are seeking rewarding careers that help us move towards more sustainable lifestyles and greener economies. This interactive online quiz helps youth match their personality with an array of green job opportunities. If you’d like to administer this quiz to your students, you can get 30 student quiz access codes for just $2.99. You’ll assign each student a unique code and direct them to a different portal www.plt.org/mygreenjob. Once completed, the students’ quiz results are displayed to them instantly online, along with Career Facts and interactive features to learn more about skills they’ll need for specific jobs.

Additional Resources for Educators

Encouraging children to spend time outside in nature can improve their creativity and imagination, classroom performance and academic achievement, as well as their overall health and fitness.

Adopt a Tree: Encourage children to “adopt” a nearby tree. It could be a tree in their backyard, in a city park, on a street in their neighborhood, or at school. Ask students to keep a journal about their tree they have “adopted” to study. Share or adapt this Adopt a Tree Journal, suitable for grades 1-4, with your students. This 28-page guide, developed by Minnesota PLT with the Minnesota Department of Natural Resources, provides students a template to record and analyze information they collect over time. Use it to help children really get to know about that special tree in their lives over the course of a school year, or a semester.
- SCIENCE: Making scientific observations about a tree’s leaves, twigs, and fruits.
Tree Dreams: Read Tree Dreams, an eco-literacy coming of age novel for grades 8-12, written by award-winning Kristen Kaye. The story emerged from a campaign to bring tree tagging to life. Kaye’s vision was to tag trees with dreams about the way we connect to nature, to each other, and to our future. She explains that “like trees that share chemical messages through their root system for the benefit of the grove, Tree Dreamers’ tags share” messages of community that connect us all-kindness, wonder, stewardship (Kaye, treedreams.net/about).
Science of Seasons: Check out the Science of Seasons page from the Forest Service Northern Research Station. This extensive resource includes podcasts, research stories, and publications related to the impacts winter has on forest ecosystems.
Go Plant a Tree! In this short video from PBS Plum Landing, see how students work with a local arborist to plant a tree in their community.
Identify Trees from Leaves: Leafsnap is a free app that uses visual recognition software to help identify tree species from leaf photographs you take in the field.
Find Nearby Trails and Parks: AllTrails is a free app that helps users discover the outdoors.
Tree Flip-Up Diagram: Use this tree diagram to create a flip-up diagram by cutting along the dotted orange line on page one and setting page two underneath it. Or, have students create their own using this as an example portraying various elements of their adopted tree’s life, including tree parts, potential inhabitants, or life among the roots.
Tips for Taking Students Outside: Friends of the Prairie Wetlands Learning Center has put together some short, simple, and practical recommendations to help effectively incorporate use of an outdoor classroom.
A Forest Year: Check out this video, which captures 15 months of a forest’s life. This 3-minute time lapse video was created from 40,000 photographs. Photographer Samuel Orr took pictures out of the same window in his home to create this forest montage.
YouTube Dendrology: Dr. Don Leopold, State University of New York’s College of Environmental Science and Forestry professor, has identified a total of 135 tree species on YouTube. These 2-minute, high definition videos briefly summarize how to identify each tree species, its ecological characteristics and importance, and communicate fun facts. tree species are also covered.
Scratch: Using Scratch, educators of all ages and levels can program interactive stories, games, and animations and share their creations in an online community. Click on For Educators to access tips and resources for using Scratch in the classroom, including an introductory video, how-to tutorials, and a webinar.
Digital Notebook Template: Want to go paperless in your classroom and experiment with digital note keeping? Read educator Nick Mitchell’s Scientific Teacher blog for ideas to transform the way you and your students take notes.

Decision Trees in Machine Learning

The term "learning tree" also applies to decision trees in machine learning, a supervised learning approach used in statistics and data mining.

Definition

A decision tree is a simple representation for classifying examples. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.

For this section, assume that all of the input features have finite discrete domains, and there is a single target feature called the "classification". A decision tree or a classification tree is a tree in which each internal (non-leaf) node is labeled with an input feature. The arcs coming from a node labeled with an input feature are labeled with each of the possible values of the target feature or the arc leads to a subordinate decision node on a different input feature. A tree is built by splitting the source set, constituting the root node of the tree, into subsets-which constitute the successor children. The recursion is completed when the subset at a node has all the same values of the target variable, or when splitting no longer adds value to the predictions. The dependent variable, , is the target variable that we are trying to understand, classify or generalize.

Regression Tree Analysis

Regression tree analysis is when the predicted outcome can be considered a real number (e.g. The term classification and regression tree (CART) analysis is an umbrella term used to refer to either of the above procedures, first introduced by Breiman et al.

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Algorithms and Metrics

Algorithms for constructing decision trees usually work top-down, by choosing a variable at each step that best splits the set of items. Different algorithms use different metrics for measuring "best". These generally measure the homogeneity of the target variable within the subsets. Some examples are given below. These metrics are applied to each candidate subset, and the resulting values are combined (e.g., averaged) to provide a measure of the quality of the split.

Positive Estimate

A simple and effective metric can be used to identify the degree to which true positives outweigh false positives (see Confusion matrix). In this equation, the total false positives (FP) are subtracted from the total true positives (TP). The resulting number gives an estimate on how many positive examples the feature could correctly identify within the data, with higher numbers meaning that the feature could correctly classify more positive samples.

However, it should be worth noting that this number is only an estimate. For example, if two features both had a FP value of 2 while one of the features had a higher TP value, that feature would be ranked higher than the other because the resulting estimate when using the equation would give a higher value. This could lead to some inaccuracies when using the metric if some features have more positive samples than others. To combat this, one could use a more powerful metric known as Sensitivity that takes into account the proportions of the values from the confusion matrix to give the actual true positive rate (TPR).

Gini Impurity

Gini impurity, Gini's diversity index, or Gini-Simpson Index in biodiversity research, is named after Italian mathematician Corrado Gini and used by the CART (classification and regression tree) algorithm for classification trees. Gini impurity measures how often a randomly chosen element of a set would be incorrectly labeled if it were labeled randomly and independently according to the distribution of labels in the set.

Information Gain

Used by the ID3, C4.5 and C5.0 tree-generation algorithms. Information gain is used to decide which feature to split on at each step in building the tree. Simplicity is best, so we want to keep our tree small. To do so, at each step we should choose the split that results in the most consistent child nodes. A commonly used measure of consistency is called information which is measured in bits. To build the tree, the information gain of each possible first split would need to be calculated. The best first split is the one that provides the most information gain. This process is repeated for each impure node until the tree is complete.

Variance Reduction

Introduced in CART, variance reduction is often employed in cases where the target variable is continuous (regression tree), meaning that use of many other metrics would first require discretization before being applied. This process is repeated for each impure node until the tree is complete.

Measure of "Goodness"

Used by CART in 1984, the measure of "goodness" is a function that seeks to optimize the balance of a candidate split's capacity to create pure children with its capacity to create equally-sized children. To build the tree, the "goodness" of all candidate splits for the root node need to be calculated. Compared to other metrics such as information gain, the measure of "goodness" will attempt to create a more balanced tree, leading to more-consistent decision time.

Advantages of Decision Trees

Simple to understand and interpret. People are able to understand decision tree models after a brief explanation.
Able to handle both numerical and categorical data. Other techniques are usually specialized in analyzing datasets that have only one type of variable.
Requires little data preparation. Other techniques often require data normalization.
Uses a white box or open-box model. If a given situation is observable in a model the explanation for the condition is easily explained by Boolean logic.
Possible to validate a model using statistical tests.
Performs well with large datasets.
Accuracy with flexible modeling.
In built feature selection. Additional irrelevant feature will be less used so that they can be removed on subsequent runs.
Decision trees can approximate any Boolean function e.g.

Disadvantages of Decision Trees

Trees can be very non-robust.
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot guarantee to return the globally optimal decision tree.
Decision-tree learners can create over-complex trees that do not generalize well from the training data.
For data including categorical variables with different numbers of levels, information gain in decision trees is biased in favor of attributes with more levels. To counter this problem, instead of choosing the attribute with highest information gain, one can choose the attribute with the highest information gain ratio among the attributes whose information gain is greater than the mean information gain. This biases the decision tree against considering attributes with a large number of distinct values, while not giving an unfair advantage to attributes with very low information gain.

Influence on Social Justice and Community

The learning tree motif significantly enhances audience engagement with themes of social justice and community by inviting viewers to reflect on their own experiences within broader societal contexts. By showcasing narratives that emphasize growth through adversity and collective strength, artists encourage critical discussions about systemic issues.

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