, PLEASE USE THIS DOCUMENT AS A GUIDE TO ANSWER YOUR ASSIGNMENT
Question 1
1. Study Learning Unit 1(Part 1). Consider Activities 1.2, 1.3 & 1.4 on your study guide.
Activity 1.2 Questions 2,3, and 4, page 6.
(2) What are the similarities and differences between conceptual and procedural knowledge in
Technology Education?
Conceptual and procedural knowledge in Technology Education share several important similarities
but also differ in significant ways. Both are essential types of technological knowledge and are
integral to the curriculum across various fields such as Civil Technology, Engineering Graphics and
Design, Mechanical Technology, and Electrical Technology. Each type of knowledge contributes to
the development of students' thinking skills and their ability to solve technological problems.
Conceptual knowledge stimulates thinking by focusing on understanding ideas and principles, while
procedural knowledge equips learners with the cognitive skills required for problem-solving, design,
and strategic thinking.
Despite these similarities, conceptual and procedural knowledge differ in their nature and focus.
Conceptual knowledge emphasizes understanding the "why" and "what" by exploring the
relationships among different ideas or systems. It is not just factual but centers on deeper
comprehension, such as how forces interact in hydraulic systems or how construction elements like
elevations and foundations relate. In contrast, procedural knowledge is about the "how" – it involves
methods and processes such as planning, system analysis, modeling, and applying algorithms. This
type of knowledge is developed through active involvement, often using a heuristic approach that
encourages learning by doing. While conceptual knowledge stimulates thinking, procedural
knowledge directly involves the execution of cognitive processes like problem-solving and design.
(3) Critique the role of algorithms in replacing human decision-making.
Algorithms play an increasingly prominent role in replacing human decision-making through the use
of computer programs and electronic tools that follow mathematical rules or structured procedures.
These algorithms, often applied within strategic and procedural thinking, are valued for their ability
to process information quickly and consistently. However, while they offer efficiency and
standardization, their use in decision-making—especially in contexts with significant social
implications—raises important concerns.
One of the central critiques is that algorithms, though perceived as objective, can introduce or
reinforce biases based on the data and assumptions embedded in their design. This can result in
discriminatory outcomes, particularly for groups that have historically faced inequality. For instance,
in areas such as criminal justice, the use of algorithmic tools like the COMPAS system in the United
States revealed substantial bias. The system was used to assess the likelihood of reoffending and
influenced sentencing, bail, and parole decisions. Although it was intended to be impartial, reviews
showed that it significantly overestimated the risk of reoffending for black individuals compared to
white individuals, who were more frequently labeled as low-risk. This led to disproportionately
harsher outcomes for black offenders, demonstrating how algorithms can perpetuate systemic
injustice.
, Another issue is the lack of transparency in algorithmic decision-making. These systems are often
protected for security or proprietary reasons, making it difficult for users and affected individuals to
understand or challenge the logic behind decisions. This complexity limits accountability and public
trust, especially when algorithms are used in critical areas that affect people’s lives. Overall, while
algorithms can enhance decision-making processes, their ability to fully replace human judgment is
deeply flawed when they fail to account for social dynamics, ethical considerations, and the nuanced
understanding that human decision-makers can provide.
(4) Differentiate between heuristic and metacognition in Technology.
Heuristic and metacognition are both key elements of strategic thinking within procedural
knowledge in Technology Education, but they serve distinct roles in the learning process. Heuristic
refers to a principle or approach aimed at discovery and learning through experience. It emphasizes
learning by doing, where students engage in hands-on activities to explore, experiment, and uncover
new techniques or solutions. This approach encourages creativity and problem-solving as students
actively apply procedural knowledge to develop understanding and innovate.
Metacognition, in contrast, involves the awareness and regulation of one’s own thinking processes. It
is the practice of thinking about thinking—where students not only engage in tasks but also reflect
on how they approach these tasks. In the context of Technology Education, metacognition enables
learners to plan, monitor, and evaluate their own learning strategies. It helps them assess whether
their problem-solving methods are effective and whether their goals are being met. This internal
control is crucial for refining approaches and becoming independent, self-aware learners.
In essence, heuristic learning is outwardly focused on discovering through action, while
metacognition is inwardly focused on consciously managing one’s cognitive processes during
technological tasks and decision-making.
Question 1
1. Study Learning Unit 1(Part 1). Consider Activities 1.2, 1.3 & 1.4 on your study guide.
Activity 1.2 Questions 2,3, and 4, page 6.
(2) What are the similarities and differences between conceptual and procedural knowledge in
Technology Education?
Conceptual and procedural knowledge in Technology Education share several important similarities
but also differ in significant ways. Both are essential types of technological knowledge and are
integral to the curriculum across various fields such as Civil Technology, Engineering Graphics and
Design, Mechanical Technology, and Electrical Technology. Each type of knowledge contributes to
the development of students' thinking skills and their ability to solve technological problems.
Conceptual knowledge stimulates thinking by focusing on understanding ideas and principles, while
procedural knowledge equips learners with the cognitive skills required for problem-solving, design,
and strategic thinking.
Despite these similarities, conceptual and procedural knowledge differ in their nature and focus.
Conceptual knowledge emphasizes understanding the "why" and "what" by exploring the
relationships among different ideas or systems. It is not just factual but centers on deeper
comprehension, such as how forces interact in hydraulic systems or how construction elements like
elevations and foundations relate. In contrast, procedural knowledge is about the "how" – it involves
methods and processes such as planning, system analysis, modeling, and applying algorithms. This
type of knowledge is developed through active involvement, often using a heuristic approach that
encourages learning by doing. While conceptual knowledge stimulates thinking, procedural
knowledge directly involves the execution of cognitive processes like problem-solving and design.
(3) Critique the role of algorithms in replacing human decision-making.
Algorithms play an increasingly prominent role in replacing human decision-making through the use
of computer programs and electronic tools that follow mathematical rules or structured procedures.
These algorithms, often applied within strategic and procedural thinking, are valued for their ability
to process information quickly and consistently. However, while they offer efficiency and
standardization, their use in decision-making—especially in contexts with significant social
implications—raises important concerns.
One of the central critiques is that algorithms, though perceived as objective, can introduce or
reinforce biases based on the data and assumptions embedded in their design. This can result in
discriminatory outcomes, particularly for groups that have historically faced inequality. For instance,
in areas such as criminal justice, the use of algorithmic tools like the COMPAS system in the United
States revealed substantial bias. The system was used to assess the likelihood of reoffending and
influenced sentencing, bail, and parole decisions. Although it was intended to be impartial, reviews
showed that it significantly overestimated the risk of reoffending for black individuals compared to
white individuals, who were more frequently labeled as low-risk. This led to disproportionately
harsher outcomes for black offenders, demonstrating how algorithms can perpetuate systemic
injustice.
, Another issue is the lack of transparency in algorithmic decision-making. These systems are often
protected for security or proprietary reasons, making it difficult for users and affected individuals to
understand or challenge the logic behind decisions. This complexity limits accountability and public
trust, especially when algorithms are used in critical areas that affect people’s lives. Overall, while
algorithms can enhance decision-making processes, their ability to fully replace human judgment is
deeply flawed when they fail to account for social dynamics, ethical considerations, and the nuanced
understanding that human decision-makers can provide.
(4) Differentiate between heuristic and metacognition in Technology.
Heuristic and metacognition are both key elements of strategic thinking within procedural
knowledge in Technology Education, but they serve distinct roles in the learning process. Heuristic
refers to a principle or approach aimed at discovery and learning through experience. It emphasizes
learning by doing, where students engage in hands-on activities to explore, experiment, and uncover
new techniques or solutions. This approach encourages creativity and problem-solving as students
actively apply procedural knowledge to develop understanding and innovate.
Metacognition, in contrast, involves the awareness and regulation of one’s own thinking processes. It
is the practice of thinking about thinking—where students not only engage in tasks but also reflect
on how they approach these tasks. In the context of Technology Education, metacognition enables
learners to plan, monitor, and evaluate their own learning strategies. It helps them assess whether
their problem-solving methods are effective and whether their goals are being met. This internal
control is crucial for refining approaches and becoming independent, self-aware learners.
In essence, heuristic learning is outwardly focused on discovering through action, while
metacognition is inwardly focused on consciously managing one’s cognitive processes during
technological tasks and decision-making.
