Engineering Better Learners: What Machine Learning Can Learn From Social Media’s Success
Faith Mundia
EdTech & Learning Sciences | Fay Institute of eLearning | Digital Learning Innovator
Machine learning (ML) models are increasingly being recognized for their potential to revolutionize education by offering personalized learning experiences, predicting student needs, and optimizing educational outcomes.?
By examining techniques from social media and learning engineering, these models can begin to imitate aspects of the human learning process, creating opportunities for more effective and responsive educational tools. However, to truly compete with traditional school environments, ML models must bridge critical gaps in replicating the human elements of learning—such as intrinsic motivation, critical thinking, and the social-emotional dynamics fostered by teachers. Implementing scaffolding strategies derived from cognitive science and educational psychology can help ML models more effectively support and guide students, enhancing their learning experiences in ways that align more closely with human development. This approach highlights the promise of ML in education, while also acknowledging the challenges in emulating the complex, multifaceted nature of human learning.
This article will look at these topics in detail to paint an efficient map for engineering learning for educators, researchers, and technologists who have a focus on innovation, practicality, and the future of education. Research resources links are included at the end.
The Human Element in Learning
The role of teachers in education goes far beyond the mere transmission of knowledge; they are the architects of intellectual and emotional growth in students, fostering critical thinking, creativity, and a lifelong love of learning. Emotional intelligence, as highlighted by Daniel Goleman, is essential for creating a positive learning environment where students feel safe and valued. Teachers who exhibit high emotional intelligence can manage classroom dynamics effectively and respond empathetically to their students' needs, laying the foundation for optimal learning.
Cognitive development, a concept rooted in Jean Piaget’s theories, emphasizes the importance of teachers in guiding students through various stages of intellectual growth. By recognizing and adapting to the cognitive abilities of their students, teachers provide the necessary challenges and support that drive intellectual progress. Carol Dweck’s research on growth mindset further underscores the role of teachers in motivating and inspiring students. By promoting the belief that abilities can be developed through effort, teachers help students embrace challenges and persist despite setbacks.
Social interaction, as explained by Lev Vygotsky’s social development theory, is another critical aspect of learning. Teachers act as mediators, facilitating interactions that enable students to construct knowledge collaboratively. The concept of the "zone of proximal development" illustrates how teachers can scaffold learning experiences to extend students' current abilities. Additionally, Howard Gardner’s theory of multiple intelligences underscores the importance of individual attention in the classroom. Effective teachers recognize the diverse intelligences and learning styles of their students, tailoring their instruction to meet each student’s unique needs.
Research consistently underscores the pivotal role of teachers in education. The OECD's Teaching and Learning International Survey (TALIS) and John Hattie's meta-analysis of over 800 studies on student achievement both highlight that teacher quality and the teacher-student relationship are among the most significant factors influencing educational outcomes. These findings affirm the timeless truth that teachers are indispensable in shaping the future, as they connect with students on both an emotional and intellectual level, adapt to diverse learning needs, and inspire a love of learning that extends beyond the classroom.
The Influence of Social Media Platforms on Human Learning
Social media platforms like Facebook, Instagram, and LinkedIn have fundamentally transformed how people interact, communicate, and learn. By leveraging principles from social psychology, these platforms have mastered the art of influencing user behavior on a massive scale, subtly shaping how individuals perceive themselves and others.
One of the key psychological concepts at play is Social Comparison Theory, proposed by Leon Festinger, which suggests that individuals assess their own worth based on comparisons with others. Social media intensifies this by providing constant opportunities for users to compare themselves with peers, often leading to altered self-esteem and behavior. Operant Conditioning, a concept introduced by B.F. Skinner, is also prevalent, with platforms using likes, comments, and shares as positive reinforcements that encourage frequent engagement and content creation.
Albert Bandura’s Social Learning Theory is evident in the way users observe and imitate behaviors of influencers and peers on social media, shaping attitudes and consumption patterns. Network Effects further amplify engagement by increasing the platform’s value as more users join, creating a self-reinforcing cycle of growth. Additionally, social media algorithms exploit Confirmation Bias by delivering content that aligns with users’ pre-existing beliefs, leading to echo chambers that shape opinion formation.
However, these strategies are not without their manipulative aspects. Addictive Design features, such as unpredictable social rewards, mimic the variable ratio reinforcement schedule found in gambling, making social media use highly addictive. Through Behavioral Targeting, platforms use vast amounts of data to personalize content and advertisements, subtly influencing decision-making through principles of behavioral economics. The phenomenon of Emotional Contagion, as demonstrated by the 2014 Facebook study, reveals how social media can manipulate users' emotions by altering the emotional content they encounter.
Research supports these observations. Studies have shown that social media can lead to negative social comparisons, reduced self-esteem, and addictive behavior patterns. The implications of these findings raise significant ethical concerns about the manipulation and potential harm caused by these platforms. While social media has driven unprecedented engagement and growth, it is crucial to balance these benefits with strategies that mitigate negative effects on mental health and well-being.
Correlation: Human Learning, Social Media Social Engineering, and Machine Learning Models
The interplay between human learning, social media social engineering, and machine learning models highlights the complex dynamics of how technology influences behavior. Social media platforms leverage psychological principles and machine learning to shape user experiences, often mirroring aspects of human learning in their design and operation. Understanding these correlations can provide insights into the ethical and practical implications of social media and AI in modern society.
Learning Engineering and the Intersection of Machine Learning, Social Media, and Human Learning
The intersection of machine learning (ML), social media strategies, and human learning presents a powerful opportunity to transform education. By drawing on the personalization and engagement techniques refined by social media platforms, ML models can be developed to tailor educational experiences to individual students, significantly improving learning outcomes. For instance, research by Pane et al. (2017) demonstrates that personalized learning can greatly enhance educational results. Just as social media algorithms analyze user behavior to deliver targeted content, ML models in education can adapt instructional materials to match each student's learning style, pace, and interests.
Social media's application of behavioral feedback loops, as seen in B.F. Skinner's operant conditioning, offers further insights for educational technologies. ML models can mimic these loops by reinforcing positive learning behaviors, much like social media platforms do with likes and shares. Additionally, the concept of emotional influence, supported by the 2014 Facebook study on emotional contagion by Kramer, Guillory, and Hancock, suggests that ML models could respond to students' emotional states, adjusting content to maintain motivation and reduce frustration.
The principle of social proof, rooted in Robert Cialdini’s research (1984), can also be applied to education. By incorporating social learning features that show students their peers' progress or achievements, ML models can encourage deeper engagement with the material, tapping into the power of observational learning as emphasized by Albert Bandura’s social learning theory.
However, these strategies are not without risks. The potential for manipulating student behavior, even with the best intentions, raises ethical concerns. For instance, the use of attention manipulation strategies, while effective in maintaining student focus, could negatively impact attention spans and cognitive load, as cautioned by Rosen et al. (2013). Additionally, privacy concerns arise from the extensive data collection required to implement such personalized systems.
To address these ethical challenges, it is essential to design ML models that prioritize transparency, respect student autonomy, and adhere to ethical guidelines. By incorporating student agency and ensuring algorithmic transparency, educational technologies can leverage insights from social media while safeguarding students' well-being and privacy. Balancing the powerful potential of learning engineering with a strong ethical framework will enable the creation of effective educational tools that support student growth responsibly.
Scaffolding Strategies for Machine Learning Models to Address Critical Human Learning Aspects
Machine learning (ML) models hold great potential to enhance education, but they often overlook critical aspects of human learning, such as intrinsic motivation, autonomy, critical thinking, social and emotional learning (SEL), contextual relevance, and the importance of teacher-student relationships. To address these gaps, ML models can implement scaffolding strategies—derived from cognitive science and educational psychology—that support and guide students in a way that enhances their learning experience. These strategies draw from insights into executive functioning, metacognition, and socio-emotional development.
1. Fostering Intrinsic Motivation and Autonomy
Intrinsic motivation is driven by internal desires, such as curiosity and the satisfaction of mastering a new skill, rather than external rewards. To foster intrinsic motivation and autonomy in students, ML models can use the following scaffolding strategies:
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Choice and Control: Allowing students to choose their learning paths, topics, or even the sequence in which they approach content can enhance their sense of autonomy. For example, an ML model could present a variety of learning modules and let students select which to tackle first. Deci and Ryan’s Self-Determination Theory (2000) emphasizes that giving learners a sense of control over their education increases intrinsic motivation.
Goal Setting and Reflection: Encouraging students to set their own learning goals and periodically reflect on their progress can deepen their intrinsic motivation. The ML model could prompt students to set short-term and long-term goals, providing feedback and reflection points along the way. This approach aligns with executive functioning strategies that emphasize self-regulation and goal-directed behavior (Diamond, 2013).
Example: An ML-driven platform might ask students to select a personal learning project (e.g., researching a topic of interest) and then guide them through the process, allowing them to set milestones and reflect on their progress.
2. Enhancing Critical Thinking and Metacognition
Critical thinking and metacognition are essential skills for deep learning, involving the ability to analyze, evaluate, and synthesize information as well as reflect on one’s own thinking processes. ML models can scaffold these skills through:
Prompted Reflection: Integrate reflection prompts at key moments during the learning process to encourage students to think about their thinking. For example, after completing a task, the ML model might ask the student to explain their reasoning or to consider alternative approaches. Flavell (1979) noted that metacognitive prompts can help students become more aware of their cognitive strategies and improve learning outcomes.
Socratic Questioning: Incorporate Socratic questioning techniques, where the ML model challenges students with questions that probe deeper into the subject matter. This method encourages students to analyze assumptions, consider implications, and think critically about the content. This aligns with the executive functioning strategy of cognitive flexibility, where students are trained to view problems from multiple perspectives.
Example: After a student answers a question, the ML model could follow up with questions like, “Why do you think this is the case?” or “What might happen if we considered this from another angle?”
3. Supporting Social and Emotional Learning (SEL)
Social and emotional learning is crucial for developing self-awareness, empathy, and relationship skills. While ML models are not inherently social or emotional, they can incorporate scaffolding strategies to support SEL:
Emotion Recognition and Response: Utilize natural language processing (NLP) and emotion recognition technologies to gauge a student’s emotional state based on their interactions with the platform. The ML model could then adjust its feedback, offering encouragement, breaks, or challenges based on the student’s emotional needs, which is consistent with research on the importance of emotional regulation in learning (Durlak et al., 2011).
Peer Interaction and Collaboration: Facilitate peer learning opportunities by grouping students with similar interests or learning objectives. The ML model could scaffold social interactions by providing structured collaborative tasks, promoting cooperative learning, and guiding students through the process of giving and receiving feedback. This approach mirrors Vygotsky’s emphasis on the social context of learning (1978).
Example: If the model detects signs of frustration in a student’s written responses or engagement patterns, it might offer positive reinforcement, suggest a different learning approach, or connect the student with peers or mentors.
4. Incorporating Contextual and Cultural Relevance
Learning is more effective when it is relevant to the student’s own cultural and social context. ML models can scaffold culturally relevant learning experiences through:
Contextualized Content: Tailor content to reflect the cultural backgrounds and experiences of the students. The ML model could analyze a student’s demographic data and past interactions to present examples and case studies that resonate with their personal experiences. This strategy aligns with culturally responsive teaching practices, which aim to make learning more relatable and engaging (Ladson-Billings, 1995).
Adaptive Contextual Scenarios: Use adaptive learning techniques to present real-world scenarios that are relevant to the student’s environment. This helps students apply what they learn in contexts that matter to them, enhancing the transfer of knowledge to real-life situations.
Example: For a student in a rural setting, the ML model might provide examples related to agriculture or local industries, making abstract concepts more concrete and relevant.
5. Strengthening Teacher-Student Relationships
While ML models cannot replace the nuanced and empathetic relationships between teachers and students, they can scaffold interactions that complement these relationships:
Teacher-Assisted Interventions: ML models can analyze student data to identify those who may need additional support, then flag these students for teachers. The model could also suggest specific interventions or conversation starters for teachers to use, based on the student’s recent activity and emotional state.
Virtual Mentorship and Guidance: Develop AI-driven virtual mentors that provide personalized feedback, encouragement, and guidance. These virtual mentors can complement the role of human teachers by offering additional support, particularly for students who may not have access to frequent one-on-one interactions with their teachers.
Example: An ML model could detect when a student is falling behind and notify the teacher with specific insights, such as the topics the student struggles with most, enabling more targeted and effective interventions.
Conclusion
Incorporating scaffolding strategies into machine learning models provides a crucial pathway to address the often-overlooked aspects of human learning in technology-driven education. By fostering intrinsic motivation, enhancing critical thinking, supporting social-emotional learning (SEL), ensuring contextual relevance, and reinforcing teacher-student relationships, these models can become powerful tools for creating more effective and holistic learning environments. Furthermore, by integrating insights from social media social engineering—such as personalization, engagement, and emotional influence—ML models can create more engaging and culturally relevant educational experiences.
However, these strategies must be designed to complement, rather than replace, the essential human elements of teaching and learning. When thoughtfully implemented, machine learning models can not only improve academic outcomes but also support the overall development of students, offering a balanced approach that honors both the technological and human aspects of education.
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