Digital literacy Study Guide

📖 Core Concepts Digital Literacy – Ability to find, evaluate, create, and share information using digital devices and media. Combines technical (how to use tools) and cognitive (critical thinking) skills. Six‑Skill Model – Reproduction, Photo‑visual, Branching, Information, Socio‑emotional, and Real‑time thinking literacies. Digital Citizenship – Responsible, ethical, and legal participation online; includes privacy protection, respecting intellectual property, and safe interaction. AI Literacy – Understanding basic AI techniques, how AI‑powered products work, and the ethical issues (bias, fairness, transparency). Digital Divide / Participation Gap – Access disparity (devices, broadband) vs. skill disparity (frequency and quality of use). --- 📌 Must Remember Digital literacy ≠ just “computer skills.” It also demands evaluating sources, creating multimodal content, and managing online identity. Six core skills (reproduction, photo‑visual, branching, information, socio‑emotional, real‑time) are the most widely accepted framework. DigComp 2.0 competence areas: Information & data literacy Communication & collaboration Digital content creation Safety Problem solving AI literacy levels: know → use → evaluate/create → consider ethics. Digital rights: privacy, access, freedom of expression. Participation gap matters more than simple “have‑or‑have‑not” access when predicting civic engagement. --- 🔄 Key Processes Evaluating Online Information Identify source → Check author credentials → Verify domain authority → Look for date & citations → Cross‑check with at least two independent sources. Creating Digital Content (Reproduction Literacy) Idea → Gather existing media → Combine/ remix → Apply proper attribution → Publish with metadata (title, tags, description). Developing AI‑Enhanced Solutions Define problem → Choose appropriate AI concept (e.g., recommendation, classification) → Gather training data → Build simple model → Test for bias & accuracy → Deploy with transparency note. Bridging the Digital Divide (Policy Loop) Assess access gaps → Allocate infrastructure funds → Implement community training → Monitor skill uptake → Adjust programs. --- 🔍 Key Comparisons Information Literacy vs. Media Literacy – Info: locating & evaluating facts; Media: decoding messages, visual/audio cues. Digital Natives vs. Digital Immigrants – Natives: grew up with tech (but not automatically high‑skill); Immigrants: adopted later (can reach comparable skill with training). Digital Literacy vs. AI Literacy – Digital: broader set of skills for any digital tool; AI: focused on understanding algorithmic processes and their societal impact. --- ⚠️ Common Misunderstandings “All teenagers are digitally literate.” → Skill varies; many lack critical evaluation or content‑creation abilities. Equating device ownership with digital competence. → Ownership solves the first‑level divide, not the second‑level (skill) divide. Thinking AI literacy is only for programmers. → Everyone needs basic AI concepts to assess algorithmic influence on information. --- 🧠 Mental Models / Intuition “Filter Funnel” – Treat every piece of online info as passing through a funnel: Visibility → Credibility → Relevance → Action. If any layer fails, discard. “Digital Footprint Mirror” – Imagine your online presence as a mirror; regular reflection (self‑audit) helps maintain privacy and ethical behavior. --- 🚩 Exceptions & Edge Cases Deepfakes – Even expert visual literacy can be fooled; rely on provenance (original source) and forensic tools when stakes are high. Open‑source software – Free to use, but may lack formal support; weigh security/privacy risks before adoption. Algorithmic bias – A model may be accurate overall yet systematically disadvantage a subgroup; always examine subgroup performance metrics. --- 📍 When to Use Which Choose DigComp 2.0 vs. ISTE standards – Use DigComp for policy‑level competence mapping (EU context); use ISTE when designing classroom learning objectives. Apply Information Literacy steps when researching (academic papers, fact‑checking). Employ Photo‑visual literacy for visual storytelling (social media campaigns, presentations). Use AI evaluation for product selection (e.g., deciding whether a recommendation engine aligns with ethical guidelines). --- 👀 Patterns to Recognize Repetition of credibility cues – URLs ending in .gov, .edu, or well‑known news domains often indicate higher trust. “Click‑bait” language – Excessive superlatives, all‑caps, or urgent calls to action usually signal low credibility. Algorithmic personalization – Same search results across accounts suggest low personalization; divergent results hint at filter bubbles. --- 🗂️ Exam Traps Distractor: “Digital literacy = ability to type quickly.” – Wrong; literacy is about critical evaluation and creation, not speed. Misleading choice: “AI literacy only requires knowing what AI is.” – Incomplete; higher‑order skills (evaluation, creation, ethics) are essential. Near‑miss: “The digital divide only concerns hardware access.” – Incorrect; the participation gap (skill/usage quality) is a distinct, exam‑tested concept. Trap: “Digital natives automatically have high socio‑emotional literacy online.” – False; social‑emotional skills must be taught and assessed.